{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext klab-autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.1 s\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('../data_files/4.Allstate_data/train.csv')\n",
    "test = pd.read_csv('../data_files/4.Allstate_data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 435 µs\n"
     ]
    }
   ],
   "source": [
    "# 为了显示所有的行和列\n",
    "pd.set_option('display.max_rows',None)\n",
    "pd.set_option('display.max_columns',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "      <td>188318.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>294135.982561</td>\n",
       "      <td>0.493861</td>\n",
       "      <td>0.507188</td>\n",
       "      <td>0.498918</td>\n",
       "      <td>0.491812</td>\n",
       "      <td>0.487428</td>\n",
       "      <td>0.490945</td>\n",
       "      <td>0.484970</td>\n",
       "      <td>0.486437</td>\n",
       "      <td>0.485506</td>\n",
       "      <td>0.498066</td>\n",
       "      <td>0.493511</td>\n",
       "      <td>0.493150</td>\n",
       "      <td>0.493138</td>\n",
       "      <td>0.495717</td>\n",
       "      <td>3037.337686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>169336.084867</td>\n",
       "      <td>0.187640</td>\n",
       "      <td>0.207202</td>\n",
       "      <td>0.202105</td>\n",
       "      <td>0.211292</td>\n",
       "      <td>0.209027</td>\n",
       "      <td>0.205273</td>\n",
       "      <td>0.178450</td>\n",
       "      <td>0.199370</td>\n",
       "      <td>0.181660</td>\n",
       "      <td>0.185877</td>\n",
       "      <td>0.209737</td>\n",
       "      <td>0.209427</td>\n",
       "      <td>0.212777</td>\n",
       "      <td>0.222488</td>\n",
       "      <td>2904.086186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.001149</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>0.176921</td>\n",
       "      <td>0.281143</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.069503</td>\n",
       "      <td>0.236880</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035321</td>\n",
       "      <td>0.036232</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.179722</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>147748.250000</td>\n",
       "      <td>0.346090</td>\n",
       "      <td>0.358319</td>\n",
       "      <td>0.336963</td>\n",
       "      <td>0.327354</td>\n",
       "      <td>0.281143</td>\n",
       "      <td>0.336105</td>\n",
       "      <td>0.350175</td>\n",
       "      <td>0.312800</td>\n",
       "      <td>0.358970</td>\n",
       "      <td>0.364580</td>\n",
       "      <td>0.310961</td>\n",
       "      <td>0.311661</td>\n",
       "      <td>0.315758</td>\n",
       "      <td>0.294610</td>\n",
       "      <td>1204.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>294539.500000</td>\n",
       "      <td>0.475784</td>\n",
       "      <td>0.555782</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.452887</td>\n",
       "      <td>0.422268</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.438285</td>\n",
       "      <td>0.441060</td>\n",
       "      <td>0.441450</td>\n",
       "      <td>0.461190</td>\n",
       "      <td>0.457203</td>\n",
       "      <td>0.462286</td>\n",
       "      <td>0.363547</td>\n",
       "      <td>0.407403</td>\n",
       "      <td>2115.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>440680.500000</td>\n",
       "      <td>0.623912</td>\n",
       "      <td>0.681761</td>\n",
       "      <td>0.634224</td>\n",
       "      <td>0.652072</td>\n",
       "      <td>0.643315</td>\n",
       "      <td>0.655021</td>\n",
       "      <td>0.591045</td>\n",
       "      <td>0.623580</td>\n",
       "      <td>0.566820</td>\n",
       "      <td>0.614590</td>\n",
       "      <td>0.678924</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.689974</td>\n",
       "      <td>0.724623</td>\n",
       "      <td>3864.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>587633.000000</td>\n",
       "      <td>0.984975</td>\n",
       "      <td>0.862654</td>\n",
       "      <td>0.944251</td>\n",
       "      <td>0.954297</td>\n",
       "      <td>0.983674</td>\n",
       "      <td>0.997162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980200</td>\n",
       "      <td>0.995400</td>\n",
       "      <td>0.994980</td>\n",
       "      <td>0.998742</td>\n",
       "      <td>0.998484</td>\n",
       "      <td>0.988494</td>\n",
       "      <td>0.844848</td>\n",
       "      <td>121012.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id          cont1          cont2          cont3  \\\n",
       "count  188318.000000  188318.000000  188318.000000  188318.000000   \n",
       "mean   294135.982561       0.493861       0.507188       0.498918   \n",
       "std    169336.084867       0.187640       0.207202       0.202105   \n",
       "min         1.000000       0.000016       0.001149       0.002634   \n",
       "25%    147748.250000       0.346090       0.358319       0.336963   \n",
       "50%    294539.500000       0.475784       0.555782       0.527991   \n",
       "75%    440680.500000       0.623912       0.681761       0.634224   \n",
       "max    587633.000000       0.984975       0.862654       0.944251   \n",
       "\n",
       "               cont4          cont5          cont6          cont7  \\\n",
       "count  188318.000000  188318.000000  188318.000000  188318.000000   \n",
       "mean        0.491812       0.487428       0.490945       0.484970   \n",
       "std         0.211292       0.209027       0.205273       0.178450   \n",
       "min         0.176921       0.281143       0.012683       0.069503   \n",
       "25%         0.327354       0.281143       0.336105       0.350175   \n",
       "50%         0.452887       0.422268       0.440945       0.438285   \n",
       "75%         0.652072       0.643315       0.655021       0.591045   \n",
       "max         0.954297       0.983674       0.997162       1.000000   \n",
       "\n",
       "               cont8          cont9         cont10         cont11  \\\n",
       "count  188318.000000  188318.000000  188318.000000  188318.000000   \n",
       "mean        0.486437       0.485506       0.498066       0.493511   \n",
       "std         0.199370       0.181660       0.185877       0.209737   \n",
       "min         0.236880       0.000080       0.000000       0.035321   \n",
       "25%         0.312800       0.358970       0.364580       0.310961   \n",
       "50%         0.441060       0.441450       0.461190       0.457203   \n",
       "75%         0.623580       0.566820       0.614590       0.678924   \n",
       "max         0.980200       0.995400       0.994980       0.998742   \n",
       "\n",
       "              cont12         cont13         cont14           loss  \n",
       "count  188318.000000  188318.000000  188318.000000  188318.000000  \n",
       "mean        0.493150       0.493138       0.495717    3037.337686  \n",
       "std         0.209427       0.212777       0.222488    2904.086186  \n",
       "min         0.036232       0.000228       0.179722       0.670000  \n",
       "25%         0.311661       0.315758       0.294610    1204.460000  \n",
       "50%         0.462286       0.363547       0.407403    2115.570000  \n",
       "75%         0.675759       0.689974       0.724623    3864.045000  \n",
       "max         0.998484       0.988494       0.844848  121012.250000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 221 ms\n"
     ]
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188318 entries, 0 to 188317\n",
      "Columns: 132 entries, id to loss\n",
      "dtypes: float64(15), int64(1), object(116)\n",
      "memory usage: 189.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>cat10</th>\n",
       "      <th>cat11</th>\n",
       "      <th>cat12</th>\n",
       "      <th>cat13</th>\n",
       "      <th>cat14</th>\n",
       "      <th>cat15</th>\n",
       "      <th>cat16</th>\n",
       "      <th>cat17</th>\n",
       "      <th>cat18</th>\n",
       "      <th>cat19</th>\n",
       "      <th>cat20</th>\n",
       "      <th>cat21</th>\n",
       "      <th>cat22</th>\n",
       "      <th>cat23</th>\n",
       "      <th>cat24</th>\n",
       "      <th>cat25</th>\n",
       "      <th>cat26</th>\n",
       "      <th>cat27</th>\n",
       "      <th>cat28</th>\n",
       "      <th>cat29</th>\n",
       "      <th>cat30</th>\n",
       "      <th>cat31</th>\n",
       "      <th>cat32</th>\n",
       "      <th>cat33</th>\n",
       "      <th>cat34</th>\n",
       "      <th>cat35</th>\n",
       "      <th>cat36</th>\n",
       "      <th>cat37</th>\n",
       "      <th>cat38</th>\n",
       "      <th>cat39</th>\n",
       "      <th>cat40</th>\n",
       "      <th>cat41</th>\n",
       "      <th>cat42</th>\n",
       "      <th>cat43</th>\n",
       "      <th>cat44</th>\n",
       "      <th>cat45</th>\n",
       "      <th>cat46</th>\n",
       "      <th>cat47</th>\n",
       "      <th>cat48</th>\n",
       "      <th>cat49</th>\n",
       "      <th>cat50</th>\n",
       "      <th>cat51</th>\n",
       "      <th>cat52</th>\n",
       "      <th>cat53</th>\n",
       "      <th>cat54</th>\n",
       "      <th>cat55</th>\n",
       "      <th>cat56</th>\n",
       "      <th>cat57</th>\n",
       "      <th>cat58</th>\n",
       "      <th>cat59</th>\n",
       "      <th>cat60</th>\n",
       "      <th>cat61</th>\n",
       "      <th>cat62</th>\n",
       "      <th>cat63</th>\n",
       "      <th>cat64</th>\n",
       "      <th>cat65</th>\n",
       "      <th>cat66</th>\n",
       "      <th>cat67</th>\n",
       "      <th>cat68</th>\n",
       "      <th>cat69</th>\n",
       "      <th>cat70</th>\n",
       "      <th>cat71</th>\n",
       "      <th>cat72</th>\n",
       "      <th>cat73</th>\n",
       "      <th>cat74</th>\n",
       "      <th>cat75</th>\n",
       "      <th>cat76</th>\n",
       "      <th>cat77</th>\n",
       "      <th>cat78</th>\n",
       "      <th>cat79</th>\n",
       "      <th>cat80</th>\n",
       "      <th>cat81</th>\n",
       "      <th>cat82</th>\n",
       "      <th>cat83</th>\n",
       "      <th>cat84</th>\n",
       "      <th>cat85</th>\n",
       "      <th>cat86</th>\n",
       "      <th>cat87</th>\n",
       "      <th>cat88</th>\n",
       "      <th>cat89</th>\n",
       "      <th>cat90</th>\n",
       "      <th>cat91</th>\n",
       "      <th>cat92</th>\n",
       "      <th>cat93</th>\n",
       "      <th>cat94</th>\n",
       "      <th>cat95</th>\n",
       "      <th>cat96</th>\n",
       "      <th>cat97</th>\n",
       "      <th>cat98</th>\n",
       "      <th>cat99</th>\n",
       "      <th>cat100</th>\n",
       "      <th>cat101</th>\n",
       "      <th>cat102</th>\n",
       "      <th>cat103</th>\n",
       "      <th>cat104</th>\n",
       "      <th>cat105</th>\n",
       "      <th>cat106</th>\n",
       "      <th>cat107</th>\n",
       "      <th>cat108</th>\n",
       "      <th>cat109</th>\n",
       "      <th>cat110</th>\n",
       "      <th>cat111</th>\n",
       "      <th>cat112</th>\n",
       "      <th>cat113</th>\n",
       "      <th>cat114</th>\n",
       "      <th>cat115</th>\n",
       "      <th>cat116</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>B</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>J</td>\n",
       "      <td>G</td>\n",
       "      <td>BU</td>\n",
       "      <td>BC</td>\n",
       "      <td>C</td>\n",
       "      <td>AS</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>LB</td>\n",
       "      <td>0.726300</td>\n",
       "      <td>0.245921</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>0.789639</td>\n",
       "      <td>0.310061</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.335060</td>\n",
       "      <td>0.30260</td>\n",
       "      <td>0.67135</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.569745</td>\n",
       "      <td>0.594646</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>0.714843</td>\n",
       "      <td>2213.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>T</td>\n",
       "      <td>L</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>I</td>\n",
       "      <td>K</td>\n",
       "      <td>K</td>\n",
       "      <td>BI</td>\n",
       "      <td>CQ</td>\n",
       "      <td>A</td>\n",
       "      <td>AV</td>\n",
       "      <td>BM</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>DP</td>\n",
       "      <td>0.330514</td>\n",
       "      <td>0.737068</td>\n",
       "      <td>0.592681</td>\n",
       "      <td>0.614134</td>\n",
       "      <td>0.885834</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.436585</td>\n",
       "      <td>0.60087</td>\n",
       "      <td>0.35127</td>\n",
       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
       "      <td>0.366307</td>\n",
       "      <td>0.611431</td>\n",
       "      <td>0.304496</td>\n",
       "      <td>1283.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>L</td>\n",
       "      <td>O</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>E</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>AB</td>\n",
       "      <td>DK</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>AF</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>GK</td>\n",
       "      <td>0.261841</td>\n",
       "      <td>0.358319</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>0.236924</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.289648</td>\n",
       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "      <td>3005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>T</td>\n",
       "      <td>I</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>I</td>\n",
       "      <td>K</td>\n",
       "      <td>K</td>\n",
       "      <td>BI</td>\n",
       "      <td>CS</td>\n",
       "      <td>C</td>\n",
       "      <td>N</td>\n",
       "      <td>AE</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>DJ</td>\n",
       "      <td>0.321594</td>\n",
       "      <td>0.555782</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.422268</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
       "      <td>939.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>H</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>P</td>\n",
       "      <td>F</td>\n",
       "      <td>J</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>K</td>\n",
       "      <td>G</td>\n",
       "      <td>B</td>\n",
       "      <td>H</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Y</td>\n",
       "      <td>BM</td>\n",
       "      <td>A</td>\n",
       "      <td>K</td>\n",
       "      <td>CK</td>\n",
       "      <td>0.273204</td>\n",
       "      <td>0.159990</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.473202</td>\n",
       "      <td>0.704268</td>\n",
       "      <td>0.178193</td>\n",
       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>2763.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 cat10 cat11 cat12 cat13  \\\n",
       "0   1    A    B    A    B    A    A    A    A    B     A     B     A     A   \n",
       "1   2    A    B    A    A    A    A    A    A    B     B     A     A     A   \n",
       "2   5    A    B    A    A    B    A    A    A    B     B     B     B     B   \n",
       "3  10    B    B    A    B    A    A    A    A    B     A     A     A     A   \n",
       "4  11    A    B    A    B    A    A    A    A    B     B     A     B     A   \n",
       "\n",
       "  cat14 cat15 cat16 cat17 cat18 cat19 cat20 cat21 cat22 cat23 cat24 cat25  \\\n",
       "0     A     A     A     A     A     A     A     A     A     B     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     B     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     B     A     A   \n",
       "\n",
       "  cat26 cat27 cat28 cat29 cat30 cat31 cat32 cat33 cat34 cat35 cat36 cat37  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     B     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat38 cat39 cat40 cat41 cat42 cat43 cat44 cat45 cat46 cat47 cat48 cat49  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat50 cat51 cat52 cat53 cat54 cat55 cat56 cat57 cat58 cat59 cat60 cat61  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat62 cat63 cat64 cat65 cat66 cat67 cat68 cat69 cat70 cat71 cat72 cat73  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     B   \n",
       "4     A     A     A     A     A     A     A     A     A     A     B     A   \n",
       "\n",
       "  cat74 cat75 cat76 cat77 cat78 cat79 cat80 cat81 cat82 cat83 cat84 cat85  \\\n",
       "0     A     B     A     D     B     B     D     D     B     D     C     B   \n",
       "1     A     A     A     D     B     B     D     D     A     B     C     B   \n",
       "2     A     A     A     D     B     B     B     D     B     D     C     B   \n",
       "3     A     A     A     D     B     B     D     D     D     B     C     B   \n",
       "4     A     A     A     D     B     D     B     D     B     B     C     B   \n",
       "\n",
       "  cat86 cat87 cat88 cat89 cat90 cat91 cat92 cat93 cat94 cat95 cat96 cat97  \\\n",
       "0     D     B     A     A     A     A     A     D     B     C     E     A   \n",
       "1     D     B     A     A     A     A     A     D     D     C     E     E   \n",
       "2     B     B     A     A     A     A     A     D     D     C     E     E   \n",
       "3     D     B     A     A     A     A     A     D     D     C     E     E   \n",
       "4     B     C     A     A     A     B     H     D     B     D     E     E   \n",
       "\n",
       "  cat98 cat99 cat100 cat101 cat102 cat103 cat104 cat105 cat106 cat107 cat108  \\\n",
       "0     C     T      B      G      A      A      I      E      G      J      G   \n",
       "1     D     T      L      F      A      A      E      E      I      K      K   \n",
       "2     A     D      L      O      A      B      E      F      H      F      A   \n",
       "3     D     T      I      D      A      A      E      E      I      K      K   \n",
       "4     A     P      F      J      A      A      D      E      K      G      B   \n",
       "\n",
       "  cat109 cat110 cat111 cat112 cat113 cat114 cat115 cat116     cont1     cont2  \\\n",
       "0     BU     BC      C     AS      S      A      O     LB  0.726300  0.245921   \n",
       "1     BI     CQ      A     AV     BM      A      O     DP  0.330514  0.737068   \n",
       "2     AB     DK      A      C     AF      A      I     GK  0.261841  0.358319   \n",
       "3     BI     CS      C      N     AE      A      O     DJ  0.321594  0.555782   \n",
       "4      H      C      C      Y     BM      A      K     CK  0.273204  0.159990   \n",
       "\n",
       "      cont3     cont4     cont5     cont6     cont7    cont8    cont9  \\\n",
       "0  0.187583  0.789639  0.310061  0.718367  0.335060  0.30260  0.67135   \n",
       "1  0.592681  0.614134  0.885834  0.438917  0.436585  0.60087  0.35127   \n",
       "2  0.484196  0.236924  0.397069  0.289648  0.315545  0.27320  0.26076   \n",
       "3  0.527991  0.373816  0.422268  0.440945  0.391128  0.31796  0.32128   \n",
       "4  0.527991  0.473202  0.704268  0.178193  0.247408  0.24564  0.22089   \n",
       "\n",
       "    cont10    cont11    cont12    cont13    cont14     loss  \n",
       "0  0.83510  0.569745  0.594646  0.822493  0.714843  2213.18  \n",
       "1  0.43919  0.338312  0.366307  0.611431  0.304496  1283.60  \n",
       "2  0.32446  0.381398  0.373424  0.195709  0.774425  3005.09  \n",
       "3  0.44467  0.327915  0.321570  0.605077  0.602642   939.85  \n",
       "4  0.21230  0.204687  0.202213  0.246011  0.432606  2763.85  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 89.2 ms\n"
     ]
    }
   ],
   "source": [
    "train.info()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 738 ms\n"
     ]
    }
   ],
   "source": [
    "pd.isnull(train).values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical:116 features\n",
      "time: 238 ms\n"
     ]
    }
   ],
   "source": [
    "# 统计离散型变量的数目\n",
    "cat_features = list(train.select_dtypes(include=['object']).columns)\n",
    "print('Categorical:{} features'.format(len(cat_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous: 14 features\n",
      "time: 23.3 ms\n"
     ]
    }
   ],
   "source": [
    "# 统计连续型变量的数目\n",
    "cont_features = [cont for cont in list(train.select_dtypes(include=['float64']).columns) if cont not in ['loss']]\n",
    "print('Continuous: {} features'.format(len(cont_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 599 ms\n"
     ]
    }
   ],
   "source": [
    "cat_uniques = []\n",
    "for cat in cat_features:\n",
    "    cat_uniques.append(len(train[cat].unique()))\n",
    "uniq_values_in_categories = pd.DataFrame.from_items([('cat_name',cat_features),('unique_values', cat_uniques)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat_name</th>\n",
       "      <th>unique_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cat9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cat10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cat11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cat12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cat13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cat14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cat15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cat16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cat17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cat18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>cat19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cat20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>cat21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cat22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cat23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cat24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>cat25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>cat26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cat27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>cat28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cat29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cat30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cat31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cat32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>cat33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>cat34</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>cat35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>cat36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>cat37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>cat38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>cat39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>cat40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>cat41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>cat42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>cat43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>cat44</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>cat45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>cat46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>cat47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>cat48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>cat49</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>cat50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>cat51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>cat52</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>cat53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>cat54</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>cat55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>cat56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>cat57</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>cat58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>cat59</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>cat60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>cat61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>cat62</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>cat63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>cat64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>cat65</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>cat66</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>cat67</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>cat68</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>cat69</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>cat70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>cat71</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cat72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>cat73</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>cat74</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>cat75</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>cat76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>cat77</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>cat78</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>cat79</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>cat80</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>cat81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>cat82</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>cat83</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>cat84</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>cat85</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>cat86</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>cat87</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>cat88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>cat89</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>cat90</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>cat91</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>cat92</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>cat93</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>cat94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>cat95</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>cat96</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>cat97</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>cat98</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>cat99</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>cat100</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>cat101</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>cat102</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>cat103</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>cat104</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>cat105</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>cat106</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>cat107</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>cat108</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>cat109</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>cat110</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>cat111</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>cat112</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>cat113</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>cat114</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>cat115</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>cat116</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cat_name  unique_values\n",
       "0       cat1              2\n",
       "1       cat2              2\n",
       "2       cat3              2\n",
       "3       cat4              2\n",
       "4       cat5              2\n",
       "5       cat6              2\n",
       "6       cat7              2\n",
       "7       cat8              2\n",
       "8       cat9              2\n",
       "9      cat10              2\n",
       "10     cat11              2\n",
       "11     cat12              2\n",
       "12     cat13              2\n",
       "13     cat14              2\n",
       "14     cat15              2\n",
       "15     cat16              2\n",
       "16     cat17              2\n",
       "17     cat18              2\n",
       "18     cat19              2\n",
       "19     cat20              2\n",
       "20     cat21              2\n",
       "21     cat22              2\n",
       "22     cat23              2\n",
       "23     cat24              2\n",
       "24     cat25              2\n",
       "25     cat26              2\n",
       "26     cat27              2\n",
       "27     cat28              2\n",
       "28     cat29              2\n",
       "29     cat30              2\n",
       "30     cat31              2\n",
       "31     cat32              2\n",
       "32     cat33              2\n",
       "33     cat34              2\n",
       "34     cat35              2\n",
       "35     cat36              2\n",
       "36     cat37              2\n",
       "37     cat38              2\n",
       "38     cat39              2\n",
       "39     cat40              2\n",
       "40     cat41              2\n",
       "41     cat42              2\n",
       "42     cat43              2\n",
       "43     cat44              2\n",
       "44     cat45              2\n",
       "45     cat46              2\n",
       "46     cat47              2\n",
       "47     cat48              2\n",
       "48     cat49              2\n",
       "49     cat50              2\n",
       "50     cat51              2\n",
       "51     cat52              2\n",
       "52     cat53              2\n",
       "53     cat54              2\n",
       "54     cat55              2\n",
       "55     cat56              2\n",
       "56     cat57              2\n",
       "57     cat58              2\n",
       "58     cat59              2\n",
       "59     cat60              2\n",
       "60     cat61              2\n",
       "61     cat62              2\n",
       "62     cat63              2\n",
       "63     cat64              2\n",
       "64     cat65              2\n",
       "65     cat66              2\n",
       "66     cat67              2\n",
       "67     cat68              2\n",
       "68     cat69              2\n",
       "69     cat70              2\n",
       "70     cat71              2\n",
       "71     cat72              2\n",
       "72     cat73              3\n",
       "73     cat74              3\n",
       "74     cat75              3\n",
       "75     cat76              3\n",
       "76     cat77              4\n",
       "77     cat78              4\n",
       "78     cat79              4\n",
       "79     cat80              4\n",
       "80     cat81              4\n",
       "81     cat82              4\n",
       "82     cat83              4\n",
       "83     cat84              4\n",
       "84     cat85              4\n",
       "85     cat86              4\n",
       "86     cat87              4\n",
       "87     cat88              4\n",
       "88     cat89              8\n",
       "89     cat90              7\n",
       "90     cat91              8\n",
       "91     cat92              7\n",
       "92     cat93              5\n",
       "93     cat94              7\n",
       "94     cat95              5\n",
       "95     cat96              8\n",
       "96     cat97              7\n",
       "97     cat98              5\n",
       "98     cat99             16\n",
       "99    cat100             15\n",
       "100   cat101             19\n",
       "101   cat102              9\n",
       "102   cat103             13\n",
       "103   cat104             17\n",
       "104   cat105             20\n",
       "105   cat106             17\n",
       "106   cat107             20\n",
       "107   cat108             11\n",
       "108   cat109             84\n",
       "109   cat110            131\n",
       "110   cat111             16\n",
       "111   cat112             51\n",
       "112   cat113             61\n",
       "113   cat114             19\n",
       "114   cat115             23\n",
       "115   cat116            326"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.9 ms\n"
     ]
    }
   ],
   "source": [
    "uniq_values_in_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA78AAAHrCAYAAAAHYCpSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmcXFWd///3UVTUUUYx31HkNwZ/Ligz4pJRHGVGFldAcZtRcRxUxnUU9Ts6AQQRUBbZkwACQpAtbCEhNNkTsnTWztbp7J2kk3Q6Sy9Jr+n9fP/oqk51dS33Vt2tbr2ejwcPOreq7j1Vdeve8znL5xhrrQAAAAAAiLNXhF0AAAAAAAD8RvALAAAAAIg9gl8AAAAAQOwR/AIAAAAAYo/gFwAAAAAQewS/AAAAAIDYI/gFAJQlY8ylxpgpYZfDKWPMtcaYa8Muh9eMMd8wxkzI8Xgs3zcAIHgnhF0AAABQvqy1UySVTCMEAKB00fMLAAAAAIg9gl8AANIYY75tjKkzxuwxxlyasv1qY8zBxH8/zLc9y76/Y4y5L+Xf040xn0n8fY0x5oAxpt4Y8x959vMpY8zLKf+enCyrMeYLxpjNiX1dm/KcyxL7bjTG/N7B/jcaYyqNMYdSn59j/5ONMT82xjxkjNmRa/8pr7nUGDM5bdtFxph9xpgaSR92sh8AAPIh+AUAIIUx5nRJN0v6V0mflHS9MeYfjTFvlnSVpNMlfVDS5xPPz7g9hxcknZd47asTr1lgjPl7SZ+S9B5JZ0n6U4HlHyNpgqTPSvr/Jf2bMeZDiYdvl/RpSadJ+oAx5g15dvc+Sb/Q0Hv7D2PMWXn2L0lXSKqU9LECy/9aSQ9J+qqGPo8PFrIfAADSMecXAICRPi1phrV2jyQZY6ZK+oykOyRtl3SrpNmSLkk8/2iW7RlZa48aY3YYY94v6W2S5ltr+yXtNcZcLumXGgr6/s5luU3i/2dJeruk1Yl/v0bSGZLWSVoi6QZJ0yR931rbnmef1dba1ZJkjHkpse8xOfYvSS9Za//isuyp3iup0Vq7KnHcqUXsCwCAYfT8AgCQm5VkrLWDkj4q6VlJ/yJprTHm1dm259nns5K+kPjvWUkyxnxS0lRJOyTlHPKcxdsT/zeSFlpr32qtfaukv5f0fOKxL0qaqKEAc6Mx5i159mlS/n6FpME8+5ekFQWUPf2Ygyn/Hsz2RAAA3CD4BQBgpHmSLjTG/L0x5u2SviJptjHmPZIWa2hI71WSTpH05mzb8xxjmqTzJX08cTxpqFd1raSnJH3NQTnbJP1/ZsgHNTRMWxoKPj9sjHm/MeZESfMlnWeMeb2Geqg3SbpOUpekd+U5xgeMMR9LDO3+gqRl2fbvoLxObZf0NmPMhxLHvdjDfQMAyhjDngEA5ezrxpjU4Gq8tfZOY8x4DQW0RtI11tqNkmSMWSBpZ+K5E621ByUdzLI9K2ttizHGStphre1NbH5W0n9KapD0pKQOY8x7rLXbs+xmnaRqSSslbVWi99Vae9gY830NBdgnSXrMWvtCovx3SVov6dWSZuj40OVs1ku6XtKZkiZZa6sS+8m4fy9YazuNMf+VKF+nhoJhAACKZqy1YZcBAABEjDHmU5KutdZ+KuSiAADgCYY9AwAAAABij55fAAAAAEDs0fMLAAAAAIg9gl8AAAAAQOwR/AIAAAAAYi/2Sx295S1vsWPHjg27GAAAAAAAH6xZs6bJWjsm3/NiH/yOHTtWVVVVYRcDAAAAAOADY8weJ89j2DMAAAAAIPYIfgEAAAAAsUfwCwAAAACIvdjP+QUAAACActHX16f6+np1d3eHXRTPnXjiiTr11FP1qle9qqDXE/wCAAAAQEzU19frDW94g8aOHStjTNjF8Yy1Vs3Nzaqvr9dpp51W0D4Y9gwAAAAAMdHd3a2TTz45VoGvJBljdPLJJxfVo03wCwAAAAAxErfAN6nY90XwCwAAAADw1Kc+9amwizAKwS8AAAAAIPZIeAUAAAAAMfT7GZu0uaHN032+/5Q36ncXneH4+T09Pbr00ku1d+9eveMd79DkyZM1ODior3/962pubtYpp5yiKVOmqL+/f9S2E07wNlyl5xcAAAAA4IsHHnhA73//+1VZWal3v/vdevjhh7Vp0yYZY7Rs2TJddtll6ujoyLjNa/T8AgAAAEAMuemh9cvmzZv1la98RZL08Y9/XDNnztQPfvAD/eM//qMuuugivfOd79R5552nD3/4w6O2eY2eXwAAAACAL8444wytWLFCkrRixQqdccYZWr9+vc466yzNmDFDTU1NWrx4ccZtXiP4BQAAAAD44rLLLtOmTZv0iU98Qtu3b9ell16q0047TRMmTNBHP/pRNTQ0aNy4cRm3ec1Yaz3faZSMGzfOVlVVhV0MAAAAAPDdli1b9L73vS/sYvgm0/szxqyx1uaNlun5BQAAAADEHsEvAAAAACD2CH4BIIOquhaNHV+hmv2tYRcFAADAlbhObS32ffkS/BpjXmWMmZH42xhjHjHGrDDGvGCMOcEYc6Ix5kVjzAZjzKOJ5xS8zY/3AKC8zd18SJK0tLYp5JIAAAA4d+KJJ6q5uTl2AbC1Vs3NzTrxxBML3ofn6/waY14raaWk9yQ2fULSCdbas4wxL0v6jKRTJNVbay80xrwo6dOS/r6IbXO8fh8AAAAAUGpOPfVU1dfXq7GxMeyieO7EE0/UqaeeWvDrPQ9+rbXHJH3AGFOb2HRI0l2Jv3sT/z9X0nOJvxdIOkfSO4rYRvALAAAAoOy96lWv0mmnnRZ2MSLJ9zm/1tod1tpVxpgvS3q1pNmSTpaUnEjXJunNRW4bwRjzA2NMlTGmKo4tHgAAAAAAdwJJeGWM+aKkyyVdZK0dkNQk6aTEwycl/l3MthGstfdba8dZa8eNGTPG+zcEAAAAACgpvge/xpi3Svq1pAuste2JzfM1NPdXGhoCvbDIbQAAAAAAZBVEz+9/SnqbpNnGmKXGmO9JelzS240x1ZJaNBTQFrMNAAAAAICsPE94lWStfVfi/zdLujnDUy5M+3dPEdsAAAAAAMgqkDm/AAAAAACEieAXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+AQAAAACxR/ALABnYsAsAAAAATxH8AkAOJuwCAAAAwBMEvwAAAACA2CP4BQAAAADEHsEvAAAAACD2CH4BIANrSXkFAAAQJwS/AJCDIeMVAABALBD8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+AQAAAACxR/ALAAAAAIg9gl8AyMDasEsAAAAALxH8AkAORibsIgAAAMADBL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+AQAAAACxR/ALAAAAAIg9gl8AAAAAQOwR/AIAAAAAYo/gFwAAAAAQewS/AAAAAIDYI/gFgAxs2AUAAACApwh+ASAHY8IuAQAAALxA8AsAAAAAiD2CXwAAAABA7BH8AkAGlkm/AAAAsULwCwAAAACIPYJfAABy2NnYoS9NXKq27r6wiwIAAIpA8AsAQA63z92uDfWtWrStMeyiAACAIhD8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+AQAAAACxR/ALAAAAAIg9gl8AAAAAQOwR/AIAAAAAYo/gFwAAAAAQewS/AJCBlQ27CAAAAPAQwS8AZGATsa8xJtyCAAAAwBMEvwCQA6EvAABAPBD8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9nwJfo0xrzLGzEj8faIx5kVjzAZjzKNmiKfb/HgPAAAAAID48Dz4Nca8VtIaSZ9ObPq2pHpr7ZmS3pTY7vU2AAAAAACy8jz4tdYes9Z+QFJ9YtO5kuYm/l4g6RwftgEAAAAAkFUQc35PltSa+LtN0pt92DaCMeYHxpgqY0xVY2Ojp28GAAAAAFB6ggh+mySdlPj7pMS/vd42grX2fmvtOGvtuDFjxnj6ZgAAAAAApSeI4He+pM8k/j5X0kIftgEAAAAAkFUQwe/jkt5ujKmW1KKh4NXrbQAAAAAAZHWCXzu21r4r8f8eSRemPez1NgDwBYupAQAAxEMQPb8AAAAAAISK4BcAAAAAEHsEvwAAAACA2CP4BQAAAADEHsEvAAAAACD2CH4BIANrbdhFAAAAgIcIfgEgB1Y6AgAAiAeCXwAAAABA7BH8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+ASADVvkFAACIF4JfAMjBGFb6BQAAiAOCXwAAAABA7BH8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+AQAAAACxR/ALAAAAAIg9gl8AAAAAQOwR/AIAAAAAYo/gFwAysDbsEgAAAMBLBL8AkIMxYZcAAAAAXiD4BYAMrOj6BQAAiBOCXwDIgY5fAACAeCD4BQAAAADEHsEvAAAOMBAeAIDSRvALAEAODH0HACAeCH4BAAAAALFH8AsAAAAAiD2CXwAAAABA7BH8AgAAAABij+AXAAAAABB7BL8AAAAAgNgj+AUAAAAAxB7BLwAAAAAg9gh+Ebq+gUEt2Hoo7GIAAAAAiDGCX4Tu9rnb9b3JVVpW2xR2UQAAAADEFMEvQre3uUuS1NLVG3JJgOOsDbsEAAAA8BLBLwDkYkzYJQAAAIAHCH4BAAAAALFH8AsAGTDqGQAAIF4IfgEgBwY9AwAAxAPBLwAAAAAg9gh+AQAAAACxR/ALAAAAAIg9gl8AAAAAQOwR/AIAAAAAYo/gFwAAAAAQewS/AAAAAIDYI/gFAAAAAMQewS8AAAAAIPYIfgEgA2vDLgEAAAC8FEjwa4x5vTFmujGm0hhzizHmLcaYJcaYjcaYmxLPKXgbAPjFmLBLAAAAAC8E1fN7iaQV1tpPSDpD0p8lVUg6U9LnjTHvkfSLIrYBAAAAAJBVUMFvj6TXGWOMpBMl/bOkudbaQUmLJJ0j6dwitqGEWTG+FEB0cYUCACAeggp+n5D0eUlbJG2V1CapNfFYm6Q3Szq5iG2IASPGlwKILq5QAACUtqCC3ysk3WetPV1Dwep7JJ2UeOwkSU2J/wrdNoIx5gfGmCpjTFVjY6P37wYAAABAbF35/EZNrtwddjHgsaCC3zdI6k783SNpuaTPGGNeIelfJS2UNL+IbSNYa++31o6z1o4bM2aMv+8MAAAAQKw8sXKvrp2xOexiwGNBBb+TJP3YGLNc0mslfVnSFyRVS6qw1tZKuruIbQAAAAAAZHVCEAex1tZJ+kTa5rPTntNU6DYA8B5pjgAAAOIkqJ5fAChJJGIDAACIB4JfAAAAAEDsEfwCAAAAAGKP4BcAAAAAEHsEvwAAAACA2CP4BQAAAADEHsEvAGRgWekIAAAgVgh+ASAHw0pHAAAAsUDwCwAAAACIPYJfAAAAAEDsEfwCAAAAAGKP4BehI7EQAAAAAL8R/CJ0yeCXxEIAAAAA/ELwi8gg9gUAAADgF4JfAAAAAEDsEfwCAAAAAGKP4BcAAAAAEHsEvwAAAACA2CP4BQAAAADEHsEvAGTA+tMAAADxQvALABlYDUW/LMEFAAAQDwS/AJCDIfoFAACIBYJfAAAAAEDsEfwCAOAA08ABAChtBL8AAOTAyHcAAOKB4BcAAAAAEHsEvwAAAACA2CP4BQAAAADEHsEvAAAAACD2CH4BAAAAALFH8AsAAAAAiD2CX4TOsnomAAAAAJ8R/CIyDItpAgAAAPAJwS8AAAAAIPYIfgEAAAAAsUfwCwAZWKaix94dc7drxoaGsIsBBGrDvqPq7hsIuxgAEAqCXwDIwYjJ6HF11/wd+tmT68IuBhCY+iNd+tKkSl09rSbsogBAKAh+AQAAykDbsX5J0sb9rSGXBADCQfALAAAAAIg9gl8AyIApvwAAAPFC8AsAuTDlFwAAIBYIfgEAAAAAsUfwCwAAAACIPYJfAAAAAEDsEfwCAAAAAGKP4Behs6TVBQAAAOAzgl9ECGl1AQDwi2URNwBljuAXAACgjBhDYzOA8kTwCwAAAACIPYJfhI5BWIgi5qIDAADEC8EvIoNRWIgiTksAAIB4IPgFAAAAAMQewS8AAAAAIPYIfgEAAAAAsec4+DXGvMIY80ZjzCuNMecYY97gZ8EAAAAAAPCKm57fKZLOknSrpO9LmuZLiQAAAAAA8Jib4PcUa+0cSe+01n5b0t/4VCYAAAAAADzlJvhtMcZMk7TRGHOhpKM+lQkAAAAeY/1yAOXOTfD7dUnXWWt/K6le0r+5OZAx5jfGmCXGmJnGmP+T+HujMeamxONvKXQbAAAAnGH9cgDlyk3w2yep1hjzSklvkjTo9IXGmHdKOsNae7akmZLulFQh6UxJnzfGvEfSL4rYBgCesqKLBAAAIE6CSnh1nqQ3GWMWSzpb0mmS5lprByUtknSOpHOL2AYAvjCGPhKgHHX3DainfyDsYgAAPBRUwqsxkhqttf8i6VRJH5XUmnisTdKbJZ1cxLYRjDE/MMZUGWOqGhsbXRQTAABAOv3qWfr4jQvCLgYAwENBJbxqk7Qt8fcuSXWSTkr8+yRJTYn/Ct02grX2fmvtOGvtuDFjxrgoJgAAwJCWzt6wiwAA8FBQCa/WSPqnxN/v0lAg/BljzCsk/aukhZLmF7ENAAAAAICs3AS/A5I+Yoy5Q9I4SZ1OX2itXS6pyRizWkOB73ckfUFStaQKa22tpLuL2AYA3iLfFQAAQKyc4OK5kyXVSpqlocRXkyV92+mLrbU/Ttt0dtrjTYVuAwC/kO4KAAAgHtwEv+9IJLqSpNnGmKV+FAgAAAAAAK+5CX73GWOukrRcQz2/e/0pEsqNZXgpAAAAAJ+5mfN7qYYyPH818f9LfSgPyhjDSwEA8B/LlwMoV457fq21vZIm+VgWAAAAJDy9ep/eOeb1Gjf2zWEXBQBiIW/wa4xZqNF5T40ka60915dSAQAAlLnfPFctSaq76YKQSwIA8ZA3+LXWnhNEQQAAAAAA8IubOb8AAAAAAJQkgl8AAAAAQOwR/AIAAAAAYo/gFwAAAAAQewS/AAAAAIDYI/gFAAAAAMQewS8AZJBc3NyYUIsBAJ6xNv9zACDOCH4BIAObqCUS/AKIG65rcKOrt1+/emq9jnT2hl0UoGgEv4gAmqIBRBdXKADlbMqqfZq6br/umr8j7KIARSP4RWQYmqIBAAAA+ITgFwCAHGiWAwAgHgh+AQAAAACxR/ALAAAAAIg9gl+EjqUXAAAAAPiN4BeRwbw6AAAAAH4h+AUAAACQEQP0ECcEv/DdstomrdjVHHYxAAAoa5YwBkVgRUrEAcEvfPetB1fqG/evCLsYAABExqPL6zSr5kAoxzZMNAJQpk4IuwAAAADl5urpmyRJdTddEHJJAKB80PMLAAAAICdW50AcEPwCAAAAyIhB8ogTgl8AyIAGbgAAgHgh+AXgiSdX7dW+lq6wi+E5EsMAAADEA8EvgKJ19fbriqkb9c0HyOodtmeq9mnHofawiwEAiAlGQiFOyPYMoGiDiTvjkc7ecAsC/frZaklkkAUAeIt1fhEH9PwCAAAAAGKP4BcAMmBJBwAAgHgh+AWAHBjmBSAuaNQDUO4IfgEAAMoIjXoAyhXBLwDP0KkAAACAqCL4BVA0OhEAAAAQdQS/CB29hQAAAAD8RvCLyGAOEgAAAAC/EPwCAAAAAGKP4BcA0hxu69YLGxrCLgYAAAA8RPALwDNxWUNyyY6msIsAAAAAjxH8Aiga87UBAACkA63H9MiyurCLgSxOCLsAAAAA8F9MBucAkfbdh1dr68F2ffaMt+qtJ50YdnGQhp5fAACAMsJgHcA/rcf6JEmDcZkLFjMEvwAAAAAysgRxiBGCXwAAULYOt3XriZV7wy4GEHmGMQOh2tvcpTV7joRdjJLHnF8AnrExmVEWj3cBwIn/+muVNtS36lPvHaNT/va1gRyzrbsvkOMg/tbvO6pXv/IVev8pbwy7KPDZv/xpoSSp7qYLQi5JaSP4BVA0WoMBlKrmzl5J0sBgcM1ev35mQ2DHQrxdPKlSEgER4BTDnhE65pIAAMrJwdbusIsAuBaX0V0obwS/iAzWigUAAIgWQwUNMULwCwAAAACIPYJfAMCw6ev366v3Lgu7GAAAAJ4j4RUAz8Rl+nbqPPRyG+51+ZT1YRcBABAh5GZBnNDzC6BoZRYfApBUs79V09fvD7sYRSunej1BDIrByg7O8DOLNnp+AQAl71jvgF776leGXYyycuGEpZKkL33w7SGXBK7RYgn4jp9ZNNHzi9DRQIYo494Vfc+tqdf7rpml2sMdYRcFAABJ9ABHVaDBrzHml8aYecaYtxhjlhhjNhpjbko8VvA2xAPDaQAUYu7mQ5KkHYfaQy4JAKDc0eMbbYEFv8aYd0i6NPHPX0iqkHSmpM8bY95T5DYAAAAAALIKsuf3LklXJP4+V9Jca+2gpEWSzilyW9mpPdyhzQ1tYRcDGCEuI3zi8j4AAABwXCDBrzHmW5I2SNqc2HSypNbE322S3lzktvTj/cAYU2WMqWpsbPT2zUTE+bcv0hfuXhJ2MQAAgFuMi0QJsjQNIwaC6vm9UNJ5kqZI+oikt0g6KfHYSZKaEv8Vum0Ea+391tpx1tpxY8aM8fzNAAAAxF1P/4Aun7JO+1q6wi4KAHgikODXWvsta+0nJX1D0hpJkyR9xhjzCkn/KmmhpPlFbAMAACgYnbGjVdY2afr6Bl0zvSbsoiACSEyKOAhrqaO7JX1BUrWkCmttbZHbAABljgF5AAAglxOCPJi1tk7S+Yl/np32WFOh2wAgDAu2HtLpb32jTvnb14ZdlLJGjx1KTVinLA1EAMpdWD2/AOIoLjUrh+/je5OrdAGJ5wCUGNqLAJQrgl8ARYtbz5ubjJZHuvp8LAkAACglNi4dATFF8Aug5FTVtWjs+AodausOuygAEHvU5QH34tYxEBcEvwBKzl+X75EkrdjVHHJJELY/zd6qmTUHwy5G6AYHrXr6B4rax08fX6sHl+zShn1HNWNDg0cliz5LN01WZPcFEDeBJrwCAMBLkxbuHP67nGOY/32uWs+sqVfdTRcUvI+KjQdUsfHA8L8vOvMUL4pWMgzdNAA8VM73pCij5xcAgBL3zJr6sIsAIOZoH3KGzynaCH5Rlmr2t2rNniNhFyN23CSKKhXcxAB4jesKAISD4Bdl6cIJS/XVe5eFXQyE6Ehnr34/Y5P6BgZHPZY6VIk5bwBQnr58T6WumV4TdjEig2G8iAOCXwBl6YaKLXq4sk4vpcxxRLTN3XxI426Yp+6+4hI7FYrESChX5Xrqr9t7dDjBIoB4IPhF6Mr1popw9Q8O9fhy/pWO61/crKaOnsCXuCIREsoWpz6AmCH4RXRwkwUAhKQcevbL4C0CQE4EvwB8V3+kS5+9Y7EOtwfbY4fyEseEa9lU1japsrYp7GKgRDGYAUC5IvgF4LvJlXXadqhd09c1hF2UUPX2D+r2Odt0rDecOauIj0seXKlLHlwZdjFiheHtABB/BL8APBOXIXV+vY0pq/fq7gW1mrhwh09HABCkfS1d2ljfGnYxgEDQPuRMXOpCcUXwC6BkldoNprd/KMlWd9/o5ZUAlJ6zb1moiyYuDbsYGbV09mrs+Ao9XbWv6H2V2KUWiAQaC6KJ4BdAyeGGAi89U7VPg4NU7xGcIC5he5o7JUmPr9xb8D641AKIG4JfAEBZ+/Wz1Zq+YX/YxYi0o129qtnv7/DeXz29Xve+vNPXYwAAyhvBLwpyrHdAE+bvUP8AwzcBjDR/yyFNWlgb+HGLGQbf2tXnXUFi6Ov3LdeFE/wd3jt17X7dPGurr8cAgKCU2tSsckHwi4LcNX+Hbpu7Xc+sqQ+7KEDJifsN8fuPVOlPs7eFXQx4aMfhjrCL4Jswfo5klgbii593tBH8oiDHevslST19xS/ZUipxwOH2bl09rUZ99HZnle+7jOI6rFEsE1BKbo1JQwf1VSC3uDfcojwQ/CIyol7xuGbaJj26Yo8WbD0cdlFKThRbQXMVKfUGH8WyA1EyMYQh7ihUYdGLJeqJnVk1B7S5oS3sYgCBI/gFHBqI2M2/rbtP+1q6wi4GgBJ3sLVb426YG3YxECCnbXoMz46vHz22Vl+4e4mr15Tr6TDAagCxQvALlKgvTazU2bcsDLsYKABDrRElL1Y3qKmjN+xiwGNcZQBv/PcTa8MuAjxE8IuicHMNz+6mzpyPV9cf1djxFarL8zwEh14UAEFr7ugJuwhASZtZczDsIsBDBL8xdritW8d6i09IlQmV+Oh7LpGJ++VtzFFGeaAxDhit/sixsIsAAJFB8BtjH/3jfP37/cvDLsYo9Ue6VFXXEnYx4IO4JEWJwrDkwUGrtm7WnkXp6+kfiPSa8DG5bAGICLfXFGutVte1xKYOFXUEvyWgf2BQa/YcKei11fWtHpemeJ+8eaG+dl/0gvJ8uCaVtm8/uFL/88yGrI9vbmjT2PEV2tPcGYnvetLCWn3g2jk63N4ddlGQoqOnXz97ch1DSV14729nlcQ1nwFNo/GRAIVzek15sfqAvn7fcj2bGLEHfxH8loBb52zXV+9dppr90QtkveC0pWtg0Krbg3WFi0VlIDrc9NAurW3KeWN5bu3QY3M3Hxqx3Y/v28kpn5xjdLiNIMtrx3oH9LE/zivotU+v3qcZGxo0YQHL+7ixft/RsIsQCw8t3a13XlERdjF8F+WRAoDX9jQP5WapayZHSxAIfiNm+c5mtacNddx8YGgdtqYI9TR09vRLknYc7vBsn/nmEf/4sTU6/epZnh2vXESgE3OYVz2qpdoAUarljpvawx06RKNCaBrbe7RmT/apL/VHunT+7Yt0uI1RD+mue3GzorrqysyNB9R6rPipGvtauvSuq2bq6ap9kqTp6/dr7PgK33KYIL8ojIYCvELwGyFHOnv1zQdW6CePRyul+vcnr9a7r3ppxLbK2iZJ0hMr9wZWjjlpPXLILUpJyaJUFgAjBV2x/dLEpfrqvdmHQf91+R7VHu6oNJKpAAAgAElEQVTQ8+v2B1iqYMXtirinuVM/fnytfvnU+qL3tbNxqFH9xeoDkqQ75+2QJB1oJXFX2OJ23qI8EfxGSE//0DCf7YfaQy7JSPO3HlbfAM1+KA+0cCOujvUO6L5FOzUQctdhQys9unHT3TdUf6k/0hVySeAnbo/xNXvTweGGp7gj+AXgGac3xtV1Lerpj8YQtmSwS4t26SNTZm53zNuum2Zu1fT18e1RLRXFDoZxcq5nekqhPxF+WuWNwVuFKaXfzQ8fXaPzblsUdjECQfALSdKhtm4d7eoNuxgloamjV4u3N4ZaBjfJY6J28d1+qF1fv2+5/lCxJdRy5BqKnfqRrdjV7H9hgAAk80kke+mSorC0F9zJd12fsmqvvnrvsqyPO52KEqWgh7M0Pqy1uuDuJXqxuiHsovii0N9N1OprcUXwG0FhnPwf++N8feSGwrKflo+hL+bK5zfqOw+tCrUkF0+qzPucls5oNmYky7X1YLSG92fzwgbvbs5RqkiWownzd2hy5e6wi+GpZYn8CyhcKQX/Tq8h46du9LcgWfhRf+GyGT+DVtrU0KafP7ku8GNX1bXo4YjdB8iLEiyC3wjJdu4HNZQvdR6YtVaVtU1Zj80PNTp2N3Vq7PiK4SRkSV4GbeUgeUp7VRGeurZeC7ceLro88M5tc7fr2hmbwy6Gp+6cvyPsIsSGKYMwK3l183reN9crdw63dWsDy3+F4mv3LdfvY3YfgDsEvyUkyIDz6ap9uuTBlZrG3LDIW717aMmQaTHOjFqM/oFB7WvJn4Ql+ftKb+8p9Hf3q6c36LuTVxf0WmmoVTwo7d19OufWl1VdT2UskyjNJT7QekwdiaXm3IrQ20CRCv0qkxm0S2HdZb9+d1V1LdrU0OrLvp369B2L9SUHI7gAeO+EsAuA0aJQP9nXMrSkQH0LSwuUut6BwfxPCkgY5/Yts7fp/sW7Qjhy4br7gk0GVlV3RLubOnX73O2BHrfURGHEy8dvXKCxJ7+uoNcOJ3cL/22gQEZD19GhwND9F9nUHq31rceOr9AXzzxFd3/zQ8Pbsv3OvIqFv3bf0DJbdTdd4M0OC+DFeshBouHMuflbDulIBHLoDAxadfcN6PWvIdRLR89vhFAfQS7bDrbrcJv7JUJumrnVh9Jklu0GaRw8xy/pw8HzCaJ4+Xo0BqlpFOTyKeu1qwyWaqhrLm45mfR7Dadb6QirAcbpdJBCTqW8U3QCfMs1+1u1bu+R4A5YYqJYT+3s6de3H1ypPc2dox6z1mpWzQH1BdQJcKSzV99/pGo4qWCYDY1XTK3WGb+bHamRS1FB8BsjT6zcG3rFrzFircpx8tk7F+usG+ePfmB4rmppidpN1El5BgetVhaR/Tlq7zmOquvDHc4YNX0Dg6HfF5Dbb57dEOrxu/sGso42cToPOi7XtgsnLNWX7xnKku2m4XTt3iN6cElpjTDK50hnr2Y4yB3y62c2aPamgwGUKLP5Ww9raW2T/jR72+jHthzWjx5bqwkLagMpS7Ej7bysxz2zpn5on6VWOQwAwW+MXPn8Rn1pYnhzSGbVHNA//WGelu0sneyjY8dXaOKC0kkYkylHSbLSUY4XON/ec5YdP1S5W/9+/4qiElkBQbpuxmade9siHWrrLqmsxm61dffpwglLVHu48CzyYX0+T1fVu36NlyU9/epZ+tB1cz3cYzxc8uBKx8/9yj3LdIOPy/dNrtytmv3BNuz99Im1+tmT67T/aO7pb8+sqdcPH11T0DH8/sUlV5c4kOc9BKGi+oAue6TwPCBeO9B6TGPHV2h1XUvYRQkcwW+ELE/0KBVToW8vMBGKF6rqhoYKbdofXKIeL9w6x9k8x3IMLqMqrKF/OxuHhlU1tHp3I80394vzzr39R4/ppplbNehxRttSlFynOvU8i9Kc33V7j6iziPvWX5fX6caZW7RoW6Nq9rfpjnml0ZhZaGZpvxo7j/UNqGZ/a2BLZw0MWj2+co/LVx1/08n5jOXi2hmbdeGEpXmfN339fs+SeTUkAsbefu+HDEfhEtTa5f2861y/y58+sVbztkSn4XxZ7dC94cmVe0MuSfAIfiPk8inrcz4exsWCqmPpiHOvTiCSw8c9/hifXLVX//bn5Rkfq9nfqjN/P0fT07KqBx3wxu3c+fmT63Tfop2qyVIJjFLwF6SoNaQc7erVl+9ZpjN+N7vgfVwzfZP+vMib4aalsNSRn+fuhROW6lspvZ2Dg1YVGw/4cqxn1+zTVc/XOHpuprf8w0erdPrVs1wf95IHV7h+TSm5fMp6XXB3/iC5GGPHV+hXT+eur4blYGu32rudBbVnXjfH59IEr6u3X00dTD/Mh+AXGZVr5bAU/frZ6qE/clRsBwetpq3b7/najqXA6bmcrPh6HQheMXWjVu0eOawoeYTNieWMlu7I3tty0cSlOuBhT3MucfnZJ5ObZAv2ohYEeilXcpPkI+lBXlgfRzIpjKSMyWrcWEuSIs89sWqvnlzlrlfIaXKd9u7iRqkV2oNWWVt4zoajXb2qqPanMSDKUr/T5F9T10ZzacWzbpyvL9y9ZNT2GF/yR7h4UqXG3TBvxLZyee9uEPxG0FEXKdInV+4uy4ux36y1emnjgdgEixffU6lfPLVejyyrC7sogfQyHuns1Xt+O3NU0JmxPAF+xYUO1/7rcrfDA497evW+QNbvffdVL+mXT0WzN6Cc/OixzHPv5m4+pGfXuJ9bGpTmzuKWBim1pWOK5fY6Wnu4XR09/a4atlNXF8h3nfSywTyqjXA/eXytfvrE2rxzYOMi9X4V1Q6RfS1dw8Ozj29L+XfA5Q5yFNWMDQ2jrnvbDx1PbpjprfcNDJbVdIFsCH4jqH/QjvoxZ3PtjM366RNrfS7RaFG9EHrlhQ0N+snja/WXpcFmb3xuTb3+669VBb021yU3mQE37OEwqRUoP8+hdfuOqLd/UPe+nD3DYzGH9ypg9vNGWbO/VY+t2KPfPFetL6Ykwuvs6fclYVffgNXz6/arp58ba5hmbzqUcfuITKhpJ38UesK9mlcY51vTkc5e9Q0U9mWdf/tifffhVR6XKFhhn6fJoLfPhzmw6SYu2KHzb1+U8znr9x1VV28weV6C/Oy3HWzXEYeNYWffsjDQ5Rzd8utz29nYoZ89uU5n/n6Oq6WMLp5UOTxdIAKX/dCw8nFEHWjt1il/+9pQjl17uEMTFw4FDam/qRkbGnThB96Wt/cqDvMHk0s2HWoLNlj8v8+Eu+SFb0qoRmpt9ptCMQF7pjWak7+vQvY7dnyFzj39/+ihS/8p4+PZkqP85rlqVVQf0ML/+ZROe8vr3R84j/f+1v08PC/FvWEO5WvNnuNDuwupVK+uczc0PCp38rASHIYpXyLOI529unhSpT57xt/5XpbU8yCIb+Kzdy7WKSedqGVXnOfZPsNuOHHKaTmP9R5vZN5xuEPv+bs3OHrdpoYMCWnL7+dFzy9G+8b9y4d/gKmB7M+eXKdF2xuHtmdacqfIH1DUf39+XDtnbGjQN+/3JgGHk9a/Ern+u3ag9VjWFnDH61T6fAJ+9I8Z1mhOHjvPHMxsX+2CAnpwdycyVqdm2E3unzVyy0P9kRgO2yy1i1uW683Oxo7IDOEuJGBIf8kjy+o0d3Pm0QhRlmn0XZQCqK7E0NWNPl6zi7klOl1yLNtn2tA6urE4nxczTAEMul5Z6DmSr/5xuK076zrSg3kO6qZnuFwQ/JaAoM/bnr7sQ3rCuimPHV8R6PGC+sx/9uS64SWuULiP37hA/3LLy6o/0iXJ2+HVftw8jzcuRUux8y5LRRl2JI1w9/wd2tfSpcWJxsyocvP7GE7mVcCX68f1/ljvgG6etdX1/LrzblukL0+qzP/EyMn8uf/uhU0FT+Vx68ePrdFDS3d7sq9/vmlB1sdST7Haw+067YrC6yf7WroKfm1Q3P4+Zm86qPNvX6wXqxv8KVBIwpw29uPH17peR7ocR0w4RfAbWaOvNpzHwSuljzxqgZS1VpMrd6stfdmBPAWdufGAvjSp0nVrZVNHjz5588IR26J28c9WnKCKebSrV5sPeLMO96aGVl/WSYyyzp7+2GQVPufWl/Wdh3LPAZ1cuVsvbIhXBTbV06v36arnNw7/28vf4QNLdunel3dqcgFJBnc1DY3OYP78SPnuCDNrDuq6FzcHUpakZ6rqi2o8uWlWdOerFloB2n5wqNd36wFnvb/ZXPZIMI0mTo27YZ7voxjuW7Qz4/tui8hokLgg+IUryR9gxGIKz8Vh3nImQY4iWLGrRdfO2Kyrp2VeyzHbcOT/fnKdNuw7Gnim7WRp0oNuP891p9+HV+fjJSlreBbrgruX6hsPxHvNzHRXTN2YcWhdMcaOrwglYUu/g9/XtTM26+dPrgugNJn5fZv5zXPVenylu6V8nEom8ComOdIZ12Rf/ziIa3nqdcfr4zlpmBzOieDtoWOh1Iayth7r05m/n+NoBYakeVu8CzS9uofma/xMP0oh9YdM73vH4Y4MzyxOqZ1DXiL4xWg5fqxXT9+U9+XZfk+pE/SjrphEREldvf26ZdZW9fQPyFqre1/e6XgZq6dX73N9vKhdx7oTvRZHHfYOdvb0a8eh4lqKkwr5LFK/63yv9/qjDqoxKWOyiyJs8agXOWhh/1b6BwZH9Jrft2hnwftatbsl0tlOy8XsTQf1iynrNH/LIe1uKm7N4iQnDRR+Kuh34kGRk1OBNuzzf4m2L05cqv92uGJGWI3iC7ZmDwKDGd3k7H0nE4VmUl1/VK3H+nT3/B2uj97a1adZNQddv06K3uivMPUPHG+I23/0mH79bLUk53lR4oTgN6KKqZxtdxlApP4gipHvIrOnxZsKQZCcXjjbuvv00T/MG5GNc9LCWt3z8k49uXKvlu1s1s2ztuqq5zP3gqabvsH9AvIRi31du+yRKn36jsWuWiPzrj2pwnoNUsswYv5wAfvKeZyIf2u/emq95pVgspqoGz91o868bk7e0Q3Xv7jZUU9JMcFzmFIvr8X+tpK/2Uz7aTh6TO+8okKbGlq1dEeTL7krfvjoGk1b36DvP1Klc2592fP9S2kNdBG7dngZY7y8bWguenuP/8v4VNe3uh7NEWSw0N03oO9N9n/475o9LaMabdy+y+ZOf+bE/vSJtfrRY2t0oDX8RH15G8bDblnNIFmi9SmNSRMXZF8GshwQ/MbQZ+5Y7Or5P358ZKtn6gUvgr/jrMaOryioVTGpq7dfU1bt1fPr6nVjhp6UXJl11+09qsPtPbpz3vHlCZKJw/oGrHoTDQwdKTfznzy+puCyFqqYCtMDi3fp+5NX+3b89MRfuUrqZ9UjiHP+0RV70o6V+x35mdFzZDlGmrpuvy4rIlnN2PEVmrbOfUPOwdZuffj6uar1YaiXn5yO7Eh+JvmydP5l6W7925+XF12ufKIWSHlt/tbDGrTSPS/v1Lf/slI/ejS4a2/WZdM82PfTq/fl7BX0w8CgDW2uv5/X5oFBq+p6/3ua3crWQOb2sxgYtHpg8a6sCdi+eu/ynI02Xl0jCtnPvkQiy1zJWNM5vRbn4iTfwdzNh/RIyrx+J9/Lwm2j65JuG1RGNhxmfm359ec6R/AbEVsPhjd8MOrLEDy3pt7xc2+fezz4XLy9UZ+5Y9Hw3Kukjiytyde/uFnjp27UL5/yfq3dA0dHp+1/aWNhw3iCtre5S2PHV+gPL23R/AKW1pHcXdi9GKZUyG06iNb8QxnW+pXy95os29msw+3ul35wys/QZ9p698HvSxsPqKWzV48lGgnCUnu4XQsdnvNLdjTqg9fNjUSCKLe9D6XUyJmNk7eQnH/rx/y5dEGMtvzNc9WjegUXbW90vMxMPpk+06un1+jM6+ZkTca1q6lTY8dX5M1y7fTjuWnm1uEpNH66a952fXFiKWbZdmbauv36w0tbdOe80R0ENftzN656sXpCMffXPc3uM2J/8Lq52pv6OpfXuP1HjznKd/Bff63S717IPx0w1XcfLq4TAcUj+I2Iz925JOtjUWyVz3UBXFrb5Ggfs2qGhholez8y3Swbjh7T/33GXTBas79V9768U1c+v1HbD3WMCjiy9V42dYxuKfSi/mJldWUio6hfw4Ik/4bbzN7kbZCeqZReDb1PF7XpPpMWjhyemvwsrLW65MEVWpbjt9PVk7kCWNfUqenr98taq5c2epuMKa6cnhfn375Y33U42mHtnqFeozATRMHZd2utHTGawumV01ob2rDGseMrRkybyVaM/3xolc6/3d3or2xSj5Gsh7ywfqhxJ9movKmhVWPHV2hX48ghs+3d3gxZvm/RTu1r8X+4q5N8CE6++mum10Ry+aKu3qHvo6NnqNc+dZ33CycszfiaTA3Rbk7/sOuu+450Ddcrp67bnzfIT5XeaeK1BVsP6bzbXlafT3WfTMJebSJKCH4jKtMlI1fLmZeZcVMveIXsdcmOzBX49Ivmjx4bGm5dWTs03PW2OdvTX1LQheHCCUt1c47lA6r2BLNUSaYLSq63szTD52at1Z9mb9WmhvwXbUffVQD3olwBXKrUz+fyKeuH//a8cpnlyj41MfS0kKNdPa1Ge5q9m8Pe0tmrytpm/XyK+8Dps3cu1uVT1uu5tfv1k8edJW4Jkpuvs29gUJc8uGLE3Pk48yuOCnu95tvnbte7r3op0GO6aaRr7uzVRRNHV/jz1QGvfL5Gp10R7PtKdTClITcqTeLJkQ5zN4/+/N2ucVwIJ4k0Jy2s1cUerp2cK1j46/I9OvuWhfrp42t9mVvulUxDb3Nx0nObfE6uz6eYa17ypUe7erXZYfLG1EaZyix1kyOdvRo7vkIzHIza8Sqgv2LqRu1s7FRzhk4XrznJjVJuCH5joj19LdUIuGnmVj1T5Txr8bZD7TqcZVhoLvtauvxraXV4VfAiYLtt7raUww4duHdgUJMW7tSX71lW9P6D8i2Hy+nsae4azhZckaHH0qvAINNXmGk+UPKGnX7Y3v5BnX3LAi3cenjETT2ZlKUoWd6km/OpJ9FCnSvTphdufGmLxo6v8GXfi7c3qqOnX/VHjqmytjnj+RAnmSqH77yiIuuweLfmbPJvKktje4/Gjq/IOR3l7vk71DcQbHjW7WA+YLYSOf3tPLnKm2WRWrv6tNLFki+Z5Jsv7oVMFf3hxGJpJ3F6cGRl9aun16sQ2W67/YOjv+MfPJo/J8GfZm8bkewnKdkbKnnfmFCx8YAeXV4naWhKxNjxFZ7MQy00oE6+v0KGH3vx2RTau5ipB/Yr9y7TF+7OPlrS7XFrG4emQaTO3fVKS2ev1vmwNryTn3859ug6FUjwa4Y8YoxZYYx5wRjzN8aYF40xG4wxjyYeP7HQbUG8h6jo6u13NXSjaC5usOlfxH2Ldg6nUnfqfxw+/6WNB/SJmxaof2BQZ9+yUGffstDVcZxyn4Qg9zAhp0HNqN04eJmTYTpR6S2QhubUfP6uJaMqBKmfYe3hjhHDs5zK9zlnGihxfJ3fkdvbe/q1r+WYrnnBWaZuN9xUSPxdbzj/mfHnxbuyPlZVV3hF/kDrMX3noVX6xZTCKsqFyPV2l+9s1nm3vey61yrf9zP+uWqNfy779W3QZh81EyW7EhXFpwpYjs0rL2xocJV8L8xKQqZz7XuP5C/7x/44L+fj6Q0clbVNjkYIFev4NSu/5Tubsz6W6/eS7bHLM1wjluxo0i2ztmp+AWvCrt/rLsFVoe0N9ySmuzjtrcwl+ftLcnpfKGT5xihUrjPlukgfYh8VMzceGFUX/co9yyLUeZH5G62s9ScDfpQF1fP7CUknWGvPkvRGSd+TVG+tPVPSmyR9WtK3i9gWW9ba4aDmt9M26udPrs86PyMOnM79vPL5jdp/9Jhn84r8UGy7TFt3n6ubs5vF44vR3NHj6HtK9uLnqy988Lq5GbdbWZ1/+yL9VxHZhgu9fQfdSDDc6xzwge+at0PLdhYfcH3tvuwZifO9pa7EsMVdjR2RWCbi9zM2aWdjp2drtSZNWb1PUxIBo9u3OehyWoufc+3C/4aG5lVnS743s+agp9OACpXryrPVwfrYh9py90avTrveX/LgSl1wt8d1gxHLvA29o2znbqaeVa9ly/5+z8s79f1H/F0OqL27b1SA0DcwqJdqvB2l8mJ1g2cjQPoGBoevqcVcW795/wpX15QpORrGgr7EO6kBpJYpObJlerZEjTnK/9za0a9JjsxKHzWV/jkUU13M9NpF23OPTEt9TUNrt378WPCrj4QpqOD3kKS7En/3SrpWUrLGu0DSOZLOLWJb7CR/GH9evEur64aGTNQ1d2ltluETfl1QdmZpYQsiAYU0tHaiF7YdbNcDOXquguLme7rskSr9+/0rXOw7mLvKR26Yp6un5+8F/d/nhpJ8bUirFLldJ29Zht6D5LztLQ4qkU6OMeIxhzd5Lz9vJze+fL3Dhdw875i3Xd96wNkw9aT0Xt58UxWiENA60RZQQ5qToXWfu3OxPpuyZN1AhD7DhqOJa7/H3UJeDeLq7R/Ug0v8vdZ7MXy1WEEEm5kkr487Gzt0oPXY8HVp26H29CdGX8opl2spQ0n6yPXzRq0UMWFBrad1oe6+Af33E+v0rQec3/ezae7o0buvmqm/LN0tyV2P/ah9dfYO5yvJ1eGQ/AnXHxn9maQftzfARE/5HB/KP3ReS9ITK/NPcfBiTnu2y16+FWC68sx13+kyo32pLStYrECCX2vtDmvtKmPMlyW9WtIaScnxOW2S3izp5CK2xdbzGVqSguTl3LtC6m9bDxa/ZENTR48umrhUf3hpS9H7yidTRd+O+NvZh7BkR5Mvaw7ev3hXxsRabrlZpil1bpqrTJE5nrs3Mcf7wcSNfdRrU/7OdHPJuOs8lW8/4o/hoWgBDDC7JUcSODfSb7o/y5PdeMmOJl3rcikIvzVlyLqeukxaIZx+g3fM2z58qj2bZd7s1oPto4KJwUGrGx1ew5Ln1YKthzxf1uc3Lqey5Bolku0zq8vR657emJZJtl5Tr37D3ytyvfNM/vfZ6uFAJSpy3bu+OLFSH79xgefHfHDJLkeZl5OK6jFzcd3NFKwNNwRlscHh+uyDg1Y1+1uHRywcaM3f85vvXP7IDUPD5qem1SEfWZ6+xnx2qZ9t8vuvdjHtLtcxCsmKX0hDqrth3sef7OR1PR5mhE5/a7lWgJE0YkRc3g4FHQ/uMSSwhFfGmC9KulzSRZIOSzop8dBJkpoS/xW6Lf1YPzDGVBljqhobPUhKU2bC/oH0DQx6miXya/ctdzQfNtPbLu7G6u/z0zm9LdxQsbnII+W/CVVUpzSaWJc3IA9PQC/P5fojx3LOYSuEPR79SpL6B62ufWFTQcPe8r3Ve17emfWxhyvrXB8vKV8LtCRNztHbOXI5leLtbOzMWxn45VPu5hc7SaaUauz4Cl3kYHrK9rQA10hZh9tV72/NOe861W+n1WjSwtpRa8Bm4vc80b8ud7ZWc+pvdU5i7flMvUy5hlQ62bcX3PSSJCuebd19w/kLOjP8Zp6q2qfrXyz+2uw3PxMRSnLdSJ2rPLsaO4Z78sIwN3Ee52r0NpLuX7JLF05YqtUOciekB1xu5/wWq/h6ylBBkr/xfIq95zqpT7j5aJxeg/3mZI5uEI3qpSqohFdvlfRrSRdYa9slzZf0mcTD50paWOS2Eay191trx1lrx40ZM8b7NxQAty1cuZ5df6TL02VZ/HbxpEqdfvUsz/ZXzPwv1wFs3oRXhZUjU5ZLr7ywoUGVtU3asO/oiJ6HZPr/5bvc33x++oR3S+4UU3HNNpSt0KV0UqcBeNkRnHyLrcf6NHlZnf7nmdG9a/k+h7oifuNuvmPfGsc82u/d83doY57eiWxJ1Katyxx4znOQTCf9c8lXhmwyJfWR3Gf3/dPsbfmfpKHESQX1qDh83pEcQ4RH9CwGMEw20IbdtIN94No5+qc/5E5i5Yaf87prD7drYHDkmsYrdrUMrSee9txsn2kURj2fe9sinXfboqyPB93QP3lZ3ajGLavjibCSw4VzFesb968oqHMg9O+jwM/abdLUQg45fJqb4Ocke2F3U6eOpC1vV4JvI1AnBHSc/5T0NkmzE8HBo5LeboyplrRBQwHtqyV9pcBtyOGTNw+1D9TddIG/B/LgRtI/YF0NeYqaTPMsUgOLHYc71DcwqFe9Mn+7U2qrnZ/5W9KHH33/k6dJktYnhl3nmwsl5b5hpD7UP2B9DeTTy5Ot4SN1Lvn/PLNBp5x04qjXOj1GMbLtopBlTJ5cFU723UKDvFEyvOWOnn5trG/VP5560ugHizlUlo/3F0+t13v/7g0F7dPpiIXWY3064RWZn5vrW9+RPqcyQKUybzup0MDwxplbVbWnRUt+c26e/RfGySgJp/z8Ss6/fbG+94nTRo1MuHzKer067d7lZTmq648GGnwUWmU5+5aFuvILp2edtpDNnM2HNGfzIX3pg2/P+PhwDJbnWtLjciSKW2v3HtFX0jIUJ7+XzXnybNQf8WnZyQJ09Q6ox0VDgRnxt78tI4c9XJrwR4+t0Zte9yqtu+YzOZ/n9h0NDlrdULFF3/vkWJ36ptcVXsAICiT4tdbeLOnmtM1/Tvt3j6QLC9wWS919A6OTSGSRXjlxWlnJlHrfr598ektyxuf4fOe78vmNrnqCc92D+gcG9WL1AX3xzFOGt+1PnQOUeG1rWs9HY3uPTvnb1zoug1/2tXTp4kmVo7Z/7s7FmvDND+npXBkb0/79jMNKwB3znM+rTJ4KQVSGvvXgSl1+3ruHjhfQMZVynFc4DJz6BwZ1pKtPY97wGh9L5Y9p6/brF0+t1+te/UqtvPI8veHEV0k6/hvb1dSphWkNLc+uqdeza+q17YbP6TUnvNKz3i4ne/n8Xc7WkXR9bJusWI2+HuaqOCYTyIUh05q9hQy3rNnfqn94u3cNGU7nQDv1fPct6vMAABvTSURBVJae/yjy+xL1UGXw84+/OHH0/agQrcf6XGdId+uPL3mTQ2GExI8lPbFWxqe6PAPcNGDNqhmd08Pp6/NlyS/m3ur2pZc5XCnCz1EUTizf2ZyxcfOnT6zVby94n952Uv764pGu7MOgC10TeH39UT1UuVsb6o/quR//c/6dlJCgen6Rw51ZAoIpq/Jnm8um2mGihfSFwq+YutG3y8BFE5fqhov/YcS29JZlvznJ4OfUw5V1+sNLW9TQeizjxSV9GIpbxzyc95wqWdbn1tarOUMZtx5s16dTMs060dzhXStmUuoN6bY52/SrT7+noPnA6xwuF+V218X+TlIDHafHvv7FzXpk+R5VX5u7hddPhbaI/yIxz7ard0B/XrRLn/uHt+of3n7SiN/ODRWZg5lRy0IU2UTnR0+mm+Qn2Spb2fbhZx+E208i3/PTP9vUIeMXTlha0Aik/oFBveuqmfrF+e8e8VtxO/+uxDqwIyn93M067Nnm7sH0MrdDujN/P8fR8/wsQ6p8593wUncF7LvQa6HbYxX7WZXC/FNj3CWHemFDQ9HHnLSwNmtehIrqA3qlMbr7mx9yvd9Dbd05O5PS315Lhrpg8uWFjESLusASXiG7O+ftGLXNSsrQ2J5Veot1oUNLn1y1V0dztCBl8vjKPapxOPRxRtrFYn+ebIlBcnttb0wEfLfM2jZqfp21Vk9XZe4Nzfa1BnUj9kTam8g57NmDC+eEBbVZl/nyXEAX+qaO3uGPcfr6/DdRY44nCekIcX1rJ/Nf85m4sLao9cqdttQPWquu3tGfVa5XO933I8vrNHZ8xfByW3fPH30dd2u+B5+taw7O90yfSabK7HceWqXzbx85x7KYjP3J4yYz7U5aWJuxVyrd4yv2jli3MllWL85dt+JWb4zT+4naLTcqn22pTHNYsNXd7/nRFXsyz5fO8HadnBpXT8u/5GM+ThMCunX/4l0jRjl+7s7FWRuXpZGje6KwlJvfCH4jLGP24SzPTT+p8127evsHtXBb/rmcTlz1fI3jiuyom02R19jcib6KD6xztVZme6Srt39EK1pUbyNB3d8GrNW9OTINZ5NePnej2ApJ4BN8TShbJSPKdY/Jy+oCX2N0U0ObHl1e5/o7uvaFTXr/NbPVl7ZMiSfztRP7WLIj/4oC2RJsPZJW8dl+KHNm2lyZur2Q7/OYmbK0Wa5vYPH2xqxrw+fjJBDpG7AZeyjS9Q4MambNQT26vE7bPFgubxQnwwi9P+oIXvTGrK5rKSizvB9KJeDyU6bPINPc0NZjffrxY+6SSgaSUC79mCl/JxshvSzG8+vc9bzWHzmm2+aMTgT4rQeH1rpPvb/E7WzcerDdcaLP4w2WcfsUjiP4DVmuOV7X+bjswa1ztum7D3u/VmE++SqvqT/OXJ9NUGFKzgpZlscuuHvp8Bp7mTRmuJl5cuN3uItth9pzrr3p9aELXQsv6pfd1GVJrn9xs6NKeapcp1bGnjZjPK3AFLOv9m53o0OK9dV7l+nq6aPXCj7rj7nzHSbX2Swm43s+ExfU6lN/WpjzOWf8bvaIf7sN4otdgzgXJ5/MtJQMtdmen6lH5a4Mo5qCdPX0Tfrsne6mcHitu29A59z6suf73VVgI0OqBVsP62N5fkPpvPolpd9bHypiuTWn0qciBVGPsNbmHE1nJNU1D9V1Ml2mMp07D1XuHk48la/RyO339d7fztSLqUsVeuT7jwzNv121u0Vjx1d4vn+ncn4XQ6kY8kof3eInL4ZWZ9Lc4by+crSrTz98tEr7WqKTzKxYBL8hS2ZiTpetYpppjmYm2dYh7O4bkLXWl6WPnFTo2ntGXnhyDTHM9tlMWljrrmAOua2QZktSlC/hw8WTKl2tE+mHbD1MToURmEa5Y+AvS3friqnulmSI2pC78BT+xR70odfK7XnW1t0/XHktRU7eb6bnpJ+/P318dE+UmwR3K3Mst1XsqIwwhjsnbT3YnveeUKxCkzsVUplNb6jN9s24TSIUxBrHH7p+7oh/B3ENnrJ6X87EpVbShsRImr0+BBdbDrTp50+uc/x99PQP6kDr6Otqpgb67r4BR8su5Ts/Z9Uc0NjxFTrY2q3rZmQ/D/yuAyzb2ax1DkY1hV1/88KsTdmnj6R/zrubOjV70yFd+Xx4SRe9RsKrmKrYOLrlruHoMf3zTQv0iXedrL95jb9f/aPL6zJuT5+rWMjF7E+zt+l1r36l+xcWINe9sZh5EemVjrZjHszhDDBhU2o2ylk1B7MO6/SSm97xXE8d/1zuINWqsM+m2+XyE4VU6KMSMEe5IQLuTFxYq9ec4L4dfNnOkcHqfAdLoqVLPZ1vneNf77aT5drccHP6BzGsuNBVEg5mCHLy8TmJcuxcMdV5wODX0O8XNjTojFPeOPzvgUE7aonDQpx+9SxHdbGr8syNvXfRUOK6K5/fmPO36sUyifnuoU7XR0/322nRDQz/kGOub7mi57eMXJDI7FxZ26zZm/xtCc80RFHyLqmTl+slJuVqCcvEy3VVz7xujuP5GE5kSvKT6sEl7rKkZnLtC5v0YnWDfvTYGv1lafZlMdzez5NDVAutCOTL6D0lyxJOyVNzwoLaoof3VNY2OXpetreYsactx/P9lt667+2SZBGJ6H2wandL2EVwxFWm6hBOwrAbfX4/Y/T9bHDQ6k+z8y93k57wK0pDB4ljo3XtcVo/yvYbdJp4tMnD1RlS62LpxV+xq1lNHT160uHKJem5GdLdMmt0YlG/ZOr9zuWxFXtDv05l8+iKwpJqpU+tWLKjST39/qxCEjSC3zKSax0wN9bsKbxCF9Frg2bVZJ7jcveC2qIz+mW6PvuyrlzKLn+SYQhiqqnr9hf9XUxeVqf/fqL41uNssn1CB1pHJjJbXTfyfPRiaM7kZXVFvf6SRAKNXHLdKPPd093M1/HCDx9dM+LfXuULmDB/h5bvdNZQUIzTr56lXY3BD1X7tz8vz/xAVC+EWZRjkPTdh1cN//1whvmoK3e3aNLC7InIsl1D8l2bg+TFiJ1s17FsQ72PdvXqcHt3JH4CUQtWCrnvpL6FXIlHb5zpw7rEaRZvH3ktX7f3qMblyH/i1k4PruHpjVEkWhstWT/99bOjR8ntKeEpPqkY9hxRUU41/tV7s1TonPD4ZuPV+mMrdmUP6B9dsUfXp61PHCYn6wevzPF+ku7yYGmWIHX09Ovu+TtGJf/5+n2jz8fC1kssTnOn+9Z0N40gPf2Dw5W1o8eCvT4s2p4/o3EhbnOZyKm3wORpknTubcElKcmnmPcRBi8riG6G2iYPu6nBWY+WVzp6+rVwW/ZzvqOnP+883tZjmRubNzrsnQtCMctQJWU7NbYcyLzvD143NO/2o6e9uehjF6uQYd9RkPqRu020WCgn0xKW7WzSu//ub3wrw57mLn3wOmdrOGdTXT/y95dr1BpGi0tbAcFvRBW79lcYJ2ghrajFljNTkpWo8bp1+foK/xODREH6uZHMFukXt4FYun43C3NraChxS2fmCnKmPZ1z68t66xtPlCT9x19WZXhG/CUTXO1t6fItiVBM7u2RVci8vaIaXAvwD2nZuTOJU/IXP1hrRzQodvb06/UpuUaiMCXg2TX1oRw3dej7wiLno7eFuOb7aMVVdpzUB3Nlay5EVZ13081QOgh+I2p5jsyXpczrUUYrI3ADzSfjsOciatjZhp2k7jJqw7kKEvMo5GsZeqzz8XK4vFejJsLgZ/DPMLj/197dx8hR33cc/3zDQ0JowjMJDzWCtiktBFrqUtoASexCaJM0LahSQIi0tIrUqlFCojRqqkooVdI0IVEgiSqw8tBQpCAaoGDzZPNgGzCYw/iMbezz8z0/P+w97PN++8furW/vdm/37nZvdubeL8ny3Ozs7G9mvvOb+c7D77d8WNMrx10P79QDd6wOuhglgjpMXved4z1ZNKL9kuAsco8u1Lndo/EqE65My9669DybsSGv7AWAd36xrA7VoW/Cla5aw1iHBiYidkBduCAeK63HI4TV9MXq11DJjxrUZVjY1buOikIyPfMu+1KXZkd79a5EsDBBXuusdKF1+5Hhkq4Zn9vbp64mS27CepF4qU8GNmqxF1vVTffHfrjBXYKF1XL2K7xSkPxGVFiuzszu9zcotfTt1uymD2hrm+jdxqVYSgx/6eGddSxJ7ZbSGNxMy5EwbdwbXN+nCJfOkeNJy1JDc2Z3d9UeYczkXPuX4aJSLe5+onwPBs2gXo1Z1tNzZeqXz/20uV7XmEyu0IvEIU36Z6vHUXKxF0AW27d22EXgWq4kkl/U0YZd5VtMns9dD7c2oCQL11ol+b3mW88vet4LbTJ/sVzha0gnarpHw9mAChonKicL9TKzIahP/GDLvNM+sOVw1WmWy1JbgG+klqPN//qPFMDjm1VE9fUy1O7p3Qvr4nLaJV9/qs4lWbrH3qrPO+wu6aYmqXcbheQ3ooI44Wpvov4L6226oZ3FKNenXqO2z/c2Lq6D9mYUxqShZyyuNfe8FHQxgKYVlqeSwuQfAmz40QK4jRjWVpoRbUE/TVXPm0mVXuMK43lZOTR4FVGvHuKK5krUEaELEGGsY+95ro277ygRxjgGmtXYVFrX/Mfin8SClG3QI7vP7+tXKsvxL+zuC1k3mIvBnV8gItw9Mlflwqpe90CWo/Gsatoj0pk9mgt1FJbi9p+8HnQRQu/+zYcbNu+tBwYbNm8sj/l6UYnKkzskv0AAGlV9LPb9lWa0tzsWdBEWLBmhu77rtjbuBGkl2dy2tH48o+bRHcH0rYrGWO5W49/uGlvW34uiZn5/fSEGxuvX+wFWFpJfICIyEWt9kCv8iII7f94SdBGaCt3dAaiHKL3mFRZReXKH5BcIQLoB78VE6a4jgjeZzFSfCAAAIERIfoEABNUPLVCrqdQK7QMTAABEFskvEABaBEaze2ZPdN4fBwAAS8NjzwAAAACwzHro7xmLRPILAAAAAKiIro4AAAAAAAgJkl8AAAAAQEW88wsAAAAAQEiQ/AIAAAAAKorIjV+SXwAAAABA9JH8AgAAAAAq8oi89EvyCwAAAACIPJJfAAAAAEBF0bjvS/ILAAAAAFgBSH4BAAAAAJFH8gsAAAAAiDySXwAAAABARRFp7JnkFwAAAAAwn2hkvyS/AAAAAIDII/kFAAAAAFQUS2SCLkJdkPwCAAAAACoaJ/kFAAAAAESdBV2AOiH5BQAAAABEHskvAAAAACDySH4BAAAAAJFH8gsAAAAAqMgi8tIvyS8AAAAAIPJIfgEAAAAAFV14xnuDLkJdkPwCAAAAACp678knBF2EuiD5BQAAAABUFJFXfkl+AQAAAACV0eAVAAAAAGAFiEb2S/ILAAAAAIg8kl8AAAAAQEU89gwAAAAAiLyI5L4kvwAAAACAyjzoAtQJyS8AAAAAIPJIfgEAAAAAkUfyCwAAAACIvNAlv2b2HjNbb2atZvagWVTaHgMAAACA5hOVhCt0ya+k2yV1uvuVks6QdEPA5QEAAACAyLr47FODLkJdhDH5XSNpY2H4BUkfD7AsDXHZ+e+v27wuOP2UqtMs173zf//MZbr16l9fnh+rgxt+9wOL+t7aS8/VVatOr/j5Oe97t054V+WVPs9HRZ++8vwFlen6D52zoOmn1RI/n/m98/Wft3x4UfPH0t169Spt+Wr9q8FPXLa4+Edtatm3JOmKC09rcEnC6aQTyleUX1z7WxW/c8cfX1QcvvSD76t7mRai0knk+i9cu+B5zbfM1fzjx36jpulm1/FrLz130b8ZpJt//4K6zKfe5zKnnXKSpMXt70uJ5VuvXrWoZfmd8+aep/7RxWfqkx8+r+p3f/sD5cu71LrugtNP0dduurTqdOvuWL2k3ynnO7dcseDvVKrDZjvr1JMXPO9GMJMi87Ctu4fqn6RnJf1pYfjvJd1fZprPS2qR1LJq1SpvZnu7x/xnLx/2eCrj6Ux23mlf2t/vf/fz7T4ymaw4TVtvzCeTaXd3z+Vynsvl5kyTzmT9hXf6PJ7KlIyPpzK+vrXbjwxM+CMtHSXfnUymfX9vzL/7zD5/cNtRX9/aXfx+Xyzue7vH3N09lcn6pr29/s0Ne70/lnB39+GJpB8emCj5ralkfnlHp1KezmQ9ncn6VDJT/M3XDw/5eCK/HF0jU/795/b73u4xf2xH57zrqFbtQ5OeSOfLMDSRX5+JdKbKt457dEeHH+gb953tI8Xvz5bOZD2bzflEIu0dw5OeTGdL1umBvnFv6435wHhi3t96/K1OP9Q/Pmd8JpvzA30x74vF3T2/zvb1xDyTPf4bXSNTxc+raR+a9DePDXvfWNyT6dJYbOuN+ZEZ2zCeyvjXH93lo5OpkuXZ2jbg7u6xeMpTheWftnFPr28/MuSJdMbjqUzJetvXE/PUjPjvG4sXf2fDrm5PpPOxMR0T01KZrB/sH/cndnb54HjC93SN+dBE0u/ffNB3d40WpxuaSPqXH97pXSNTvrN9xPvG4j6eSPs3ntxT3F9mymZz/j+vHfXJZNr7YwnvHZu7DtOZrH/tf1u9fWiyOC6Xy/noVGrOtPPZ2jZQMo+ZHt3R4Rt2ddc0nz1dY/5Oz1jx70rznC2TzXkynfVkOutdI1Nl18ds0zG1aW+vr9tyyLtHp9w9H28z17u7+3N7ev2VAwMei6eq1nHpTHZOXbi7a7Tk78HxhK/bcsjXt+bXSyqT9Vi8/DqfSKT9kZYOf6Slo+oyzbaQ+mDa252jJXVd+9CkP/xGe8k0k8n0nLp3pnQm6996au+cen4ymS4bW1vbBvwbT+5xd/djg5Pe2jHi/bGEt/XGSqaLpzLFbTu9r+3uGvUtbf1lyzE8kfT+WMJ3HBv27z27r6QcLUeH3d39lQMDxX11tr6xuO84NlyMr8ff6vT7NrUVPy93fBqeSPr/7exyd/ctbf1+sEy9V4vB8YRPJTP+4r4+7xie9OGJpLd2jLi7+xtHhvz+zQeL007H3Ja2fk+kM57KZIvLtbN9pGS+iXTGhyeS3tYb873dY2WPr+VsOzTouzpG/dhgbfvkKwcGitsvm835L7Yd9alkPmZePTjovWNxf+3QoL96cLAk9tOZrB/oK7/O4qmM/2LbUd/VMerbjwxVLUMsnio5lkw70BfzsRm/uatj1FuO5uc3XKjTU5msb9zTW5wml8uVHAsq2d016i/tz8fj0cEJT2ey3jsWL6lT9vfG/J5n9xVjuH0of2ydWda+sXjx/KNWuVzOXzs0OGebTsdwLpfzrW0DJfvgvZva/PG3On10KlU8jo9MJj2dyfrm/f3FerJnNO77emL++uH8enqnZ8xf2Nc35zg70/R27RmNF9f3qwcH/dndPVWXZcexYU+kM55MZ30snvIdx4aLy1jtfGOmXC7nD712zCcSaU9lst4Xi/sbR4a8Z/T4Pj+eSJetm777zD6/65dvVdxH4qmM7+kamzN+Z/tIyfYut46e2d3j3376HR8cT/joVKrk3ME9v5/OjLeB8YQn0hl/u3N09qyKUpmspzJZH5k8fr6ayeYqHrPGE3Pr8daOkTnj3u4cLdkX3I+fG840NJH0u5/Y7ROJ6sff2V54p69knyxneCLpB/piPjhj+08lMz6eSPvPXj5csg6z2fz+2tYb89vWbSt7/tOsJLV4Dbmk5acNDzN7SNKj7v4rM/uKpDPd/V8rTb969WpvaWlZvgICAAAAAJaNmb3p7lVv7YfxsefnJd1YGF4j6cUAywIAAAAACIEwJr8PSbrAzHZJGlY+GQYAAAAAoKITgy7AQrl7UtKngi4HAAAAACA8wnjnFwAAAACABSH5BQAAAABEHskvAAAAACDySH4BAAAAAJFH8gsAAAAAiDySXwAAAABA5JH8AgAAAAAij+QXAAAAABB5JL8AAAAAgMgj+QUAAAAARB7JLwAAAAAg8kh+AQAAAACRR/ILAAAAAIg8kl8AAAAAQOSR/AIAAAAAIs/cPegyNJSZDUg6FnQ5qjhb0mDQhUDTI05QC+IEtSBOUAviBLUgTlCLRsfJRe5+TrWJIp/8hoGZtbj76qDLgeZGnKAWxAlqQZygFsQJakGcoBbNEic89gwAAAAAiDySXwAAAABA5JH8NocHgi4AQoE4QS2IE9SCOEEtiBPUgjhBLZoiTnjnFwAAAAAQedz5BQAAAABEHslvgMzsPWa23sxazexBM7Ogy4TlYWYnmdmTheE5cVDvcUEvLxausC3/28xeM7MnzOzXiBPMZmYnmtkjZvaKmf2U+gTzMbO7zGyTmZ1tZlvN7G0z+3bhs7qOQ/iY2U1m1mlmLxf+XUl9gnLM7J8L+/zTZnZumOoTkt9g3S6p092vlHSGpBsCLg+WgZmdIulNHd/e5eKg3uMQPh+RdKK7XyPp/ZLuFHGCuf5SUqu7f0TSeZL+ScQJyjCziyT9TeHPL0naIOlKSX9mZh9qwDiE03+5+7Xufq2kPxT1CWYxs0skXebu10l6WtIPFKL6hOQ3WGskbSwMvyDp4wGWBcvE3ePufoWkzsKocnFQ73EInz5J9xaGU5LuFnGCuZ6R9H0zO1HS6ZKuEnGC8u6V9C+F4TWSNrp7TtJmzdjWdRyHcLrFzLab2a8krRX1CeZaK+kMM9si6TpJFytE9QnJb7DOkjRWGI5JOjPAsiA45eKg3uMQMu5+wN23m9lfSTpZ+acFiBOUcPcJd5+S9IryF0yoTzCHmd0mqVXS3sIo4gTlHJL0b+5+tfJPktws4gRznSNpwN2vl3ShpKsVojgh+Q3WoKTTCsOnFf7GylMuDuo9DiFkZn8h6YuSPi2pX8QJZjGzs8zs3ZL+RPnHCC8XcYK5PqX83ZpfSvoDSWeLOMFcw5I2FYaPSsqJOMFcMUn7C8OHlY+V0MQJyW+wnpd0Y2F4jaQXAywLglMuDuo9DiFjZh+U9FVJn3T3cREnKO8rkv7a3bOSpiR9U8QJZnH32wrvcH5W+adIfizpRjN7l6SPasa2ruM4hM+XJX22sB0vV75+oT7BbG8q/z64JP2m8olwaOoTkt9gPSTpAjPbpfzVtucDLg+CUS4O6j0O4fM55R87e9bMXpZ0kogTzPVjSXea2TZJQ5J+IuIE1d0n6c8l7ZK0wd0PNmAcwudHkv5W0uuSHhP1Ccpw922SBs3sDeUT3zsUovrE3L2e8wMAAAAAoOlw5xcAAAAAEHkkvwAAAACAyCP5BQAAAABEHskvAAAAACDySH4BAIgAM/thmXF3m9nHAigOAABNh+QXAIAIcPcvBF0GAACaGckvAAARYGYvFf4/3cw2mtlmSdcHWyoAAJoHyS8AANHyeUlPuftHJeWCLgwAAM2C5BcAgGi5RFJrYfiNIAsCAEAzIfkFACBajkm6vDB8VZAFAQCgmZD8AgAQLesk3WxmL0s6NejCAADQLMzdgy4DAAAAAAANxZ1fAAAAAEDkkfwCAAAAACKP5BcAAAAAEHkkvwAAAACAyCP5BQAAAABEHskvAAAAACDySH4BAAAAAJH3/0YhjR+KRSEYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 634 ms\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['id'],train['loss'])\n",
    "plt.title('Loss values per id')\n",
    "plt.xlabel('id')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(7, 71, 'Binary features')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 495 ms\n"
     ]
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "fig.set_size_inches(16,5)\n",
    "ax1.hist(uniq_values_in_categories.unique_values, bins=50)\n",
    "ax1.set_title('Amount of categorical features with X distinct values')\n",
    "ax1.set_xlabel('Distinct values in a feature')\n",
    "ax1.set_ylabel('Features')\n",
    "\n",
    "ax1.annotate('A feature with 326 vals', xy=(322, 2), xytext=(200, 38), arrowprops=dict(facecolor='black'))\n",
    "\n",
    "ax2.set_xlim(2,30)\n",
    "ax2.set_title('Zooming in the [0,30] part of left histogram')\n",
    "ax2.set_xlabel('Distinct values in a feature')\n",
    "ax2.set_ylabel('Features')\n",
    "ax2.grid(True)\n",
    "ax2.hist(uniq_values_in_categories[uniq_values_in_categories.unique_values <= 30].unique_values, bins=30)\n",
    "ax2.annotate('Binary features', xy=(3, 71), xytext=(7, 71), arrowprops=dict(facecolor='black'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 505 ms\n"
     ]
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(1,2)\n",
    "fig.set_size_inches(16,5)\n",
    "ax1.hist(train['loss'], bins=50)\n",
    "ax1.set_title('Train Loss target histogram')\n",
    "ax1.grid(True)\n",
    "ax2.hist(np.log(train['loss']), bins=50, color='g')\n",
    "ax2.set_title('Train Log Loss target histogram')\n",
    "ax2.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a9374ad90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a9370ffd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93731610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a936dcdd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a936915d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a936bcd90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a9366f590>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93619d50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93622890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a935d5210>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a935ab510>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93556cd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93507fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a93533c90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a878a2f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a8784ec50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA64AAAK4CAYAAAB0/qBAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3X+wZHV98Pn3hwKEgI4IOloITHxYiY8MVOK4xQaNlzEQXAYtJY9rhNXRkEll4xMTpkywslrZPGEjSSiD4oOZMlu6FDFbBbJGZi2QHzeK+COSFDORRA1xKMZykXnIDl5CIKOf/aPPhb493fee23369Le736+qqen+nnNPf053f/qczznfc76RmUiSJEmSVKojJh2AJEmSJEmrsXCVJEmSJBXNwlWSJEmSVDQLV0mSJElS0SxcJUmSJElFs3CVJEmSJBXNwlXrFhEvi4h7Jx2HpMH65WlEnBUR34yIgxHx0UnFJmmlQdtVt7dSmQZsY8+IiG9ExA8i4vqIiEnFN6ssXDVQROyLiE09bR8E9gIvm0RMklZaZ57uAj4CnAwsRMRFbcQoqWM9+er2Vpq8dW5j/xT4c2ATsBV4w/gjnC8WrlqXzPx94JWTjkPSYP3yNCJOA/4j8InMXAI+BfynCYQnqcug7arbW6lMq+TmOcBNmfmvwN8CZ7Ya2BywcJ1BEXFpRHw3Ih6JiPdVbZdVR40eiojtVdtCRCxGxDUR8S8R8cWIODYifiMiDgCnAH8bEQci4rgJrpI0cyaQp/8ReDAzf1Q9/w7wijGuojQz3K5K02VCOftTmflo1UX4LOChca7jPDpy0gGoWRHxCuAPgXOBfwP2RMRtwNXAzwI/Bu6NiPuqPzkH+L+BFwN/A7whMz8CfCQi9gELmbmv1ZWQZtyE8vQEYKnr+RPA8xtZIWmGuV2VpsukcjYzH6ke/g7wJPCZptZJHZ5xnT0/D+zOzIcz81HgpcDrgM9l5kOZ+TCdRLqgmv9R4NrMfAr4O+B5kwhamjOTyNPHgOd2PT++apO0Orer0nSZWM5GxK8ClwMXZea/j7ISOpyF62zqvovZ6zn8c86ueb6bmdnVLqkdbefpN4H/EBHLPW1+CnhgyGVJ88btqjRdWs/ZiDgfuBJ4fdfZVzXIwnX23AVcFBGnRMTzgY8B3wC2RcSpEXEy8Bbgtmr+H6+yrAPAT0bEERFx4lijluZL63laHWHeC+yIiOcC/zNwUwPrIs06t6vSdGk9Z6vrWv8Y+KXM9NrWMbFwnTGZ+U3gd4EvAX8P/NfM/DKdI0BfBO4FPpiZe2ss7oPAJ4H/xrPdKSSNaIJ5+qvArwP7gTsz8/NDrYA0R9yuStNlQjn7SmAzcGt1I6cDEXH98GuhfuLZM+OSJEmSJJXHM66SJEmSpKJZuEqSJEmSirZm4Rodn4qIr0bEX0XE8RFxa0TcHxE3VNOPGbatjZWUJEmSJE2vOmdczwWOzMxz6Ixr9G5gf2aeTWdA+/OBy0ZokyRJkiRpoCPXnoVHgGurx08Dvwf8SvX8LuA84DTg5iHbbh/0wieddFJu2rRpYGBPPPEExx13XI1VGC/jKCuG0uO47777DmTmCycU0liZs8YwrXGsFsM85GwJn8GyUmIxjsOVEstacZiz7SklDignllLigHJiaSpn1yxcM/M7ABHxZuBo4D7gYDX5ceAM4MQR2laIiB3ADoCNGzfyJ3/yJwNjW1pa4vjjj19rFcbOOMqKofQ4zjvvvLGO71V1wf8knfz6AfB24C+BU4A9wDuA59AZw3PdbbnKrcg3bdrEN77xjYGxLS4usrCwMMrqNaKEOIyhrDhWiyEiZnZMvuWcLeEzWFZKLMZxuFJiWSsOc7Y9pcQB5cRSShxQTixN5WydM65ExBuB9wIXAx8HNlSTNtAZmPf4EdpWyMxdwC6ALVu25GorOS0fxjzFUUIMxvFs9/6IWOTZ7v3bIuJWOl30Tx2hbWAvCUmS5kFEHAV8JjMvjohjGPJgcL+21Q4QS/Oszs2ZXgy8D7goM38I3MmzA/BuBe4esU1Ss/p17/9C9Xy5i/7WEdokSZpbEXEsnR6Iy/dqGeVeL97/RaqpzhnXdwIvAW6rbgJ8A3ByROwB7qdTjB4NvGXINkkNmnT3/sXFxYGxLS0trTq9LSXEYQxlxVFCDJKmQ2Y+CZwVEf9UNW1l+Hu9rHn/l37b2VJ+s0qJA8qJpZQ4oJxYmoqjzjWuVwNX9zT/Wc/zp4BtQ7ZJapjd+1dXQhzGUFYcJcQgaWqNcjB4zQPE/bazpfxmlRIHlBNLKXFAObE0FUed4XAkTRG790uS1KoDHH6Qd5Q2SX3UujmTZtOmK3c/83jfhy6aYCRqmN37p1h3XoK5KTXB7Z3GbPkg7810DvJ+mM7NDYdtU8P8DZgNFq7SjLF7vyRJrbqR4Q8Ge4BYqsnCVZIkSVqnzDy9+n+Ug8EeIJZqsnCVJEmS1Cq772q9LFwlSZIkFckCV8u8q7AkSZIkqWgWrpIkSZKkolm4SpIkSZKKZuEqSZIkSSqahaskSZIkqWgWrpIkSZKkojkcjiRJkqRGdA9fAw5ho+Z4xlWSpAmLiKMi4nPV44iIT0XEVyPiryLiyIi4MCL2R8Q91b8zIuKYiLg1Iu6PiBuqvzusbdLrJklSEyxcJUmaoIg4FrgPOL9qOhc4MjPPAZ4HXFC1X5+Zr6n+fQu4DNifmWcDJ1R/369NkqSpZ+EqSdIEZeaTmXkWsL9qegS4tnr8dNesl0TE1yPi5upM6lbgC9W0u4DzBrRJkjT1vMZVkqSCZOZ3ACLizcDRwG3Ay4APZObuiLgXeB1wInCw+rPHgTMGtK0QETuAHQAbN25kcXGRpaUlFhcXx7ZO6zGOWHZuPvTM47rLLuU9KSUOKCeWUuKQ1C4LV0mSChMRbwTeC1ycmT+KiMeAO6rJ+4AXAQeADVXbhur58X3aVsjMXcAugC1btuTCwgKLi4ssLCyMZ2XWaRyxbO+6Wcy+S+stu5T3pJQ4oJxYSolDUrvsKixJUkEi4sXA+4CLMvOHVfMVwNsi4gjgTODvgTt59vrXrcDdA9okSZp6Fq6SJJXlncBLgNuqOwi/G7gOeBfwNeCWzHwAuBE4OSL2AI/RKVr7tUmSWrDpyt3s/d7Bw4YEUjPsKixJUgEy8/Tq/6uBq/vMstAz/1PAtp55+rVJkqZEd9HrGLgrWbhKkiRJQ4qI44C/AE4Cvgz8EXAL8Hxgd2ZeGREn1WmbyArMsd4zoxaKZbOrsCRJkjS8S4GvZua5wCuBPwN2A2cDb4iIlwO/WbNN0gAWrpIkSdLwngJ+ohpf+RjgZ4EvZOaPgb+ma4zlGm2SBrCrsCRJkjS8vwC+AvwnOjdEeykrx1N+AYePsTyo7TAlj73cL47ucZNh8NjJdcdXrjvfau/JoGXUjbWunZsPsfHYzv/DLmuYcacHKfl7MoxahWtEHAV8JjMvjogLgU/QGUcO4JeBh4CbgFOAPcA7gOfUacvMHHktJEmSpMl4P/DxzPxERHwaeDkrx1N+iMPHXR7UdpiSx17uF8f23utGB4ydXHd85brzrfaeDFpG3Vjr2n7lbnZuPsQ1e48celnDjDs9SMnfk2Gs2VU4Io4F7gPO72q+PjNfU/37FnAZsD8zzwZOqOat2yZJkiRNq+cC/1Y9forO2dcLqnGXX0fXGMs12iQNsGbhmplPZuZZwP6u5ksi4usRcXPVn38r8IVq2l109duv0SapYRFxVER8rnp8YUTsr8aDvCcizoiIYyLi1oi4PyJuiI5abZNeN0mSCvMx4Nci4ivAscCbgf+RTu/C3Zn5T8BHarZJGmCYa1wfBD6Qmbsj4l46R4h6++ifsY62Ffr14x9k1vpttx1Hk33oh41hXOY5jqqXxNfodFVadn1mXtU1z+V0ej9si4hb6fR+OLVm2+1trYskSaXLzH3AuT3Nr+2Z50CdNkmDDVO4PgbcUT3eB7yIw/voHwCOr9m2Qr9+/IPMWr/ttuNosg/9sDGMyzzHkZlPAmdFRPeR20si4k3Aw8Av0un9cHM1bbn3w2k12yxcJUmS1KphCtcrgG9HxA3AmcAf0ClIL6Czg7sV+DCdMzV12iSNl70kCoxjUAxN3+FwmBjaVkIcJcQgSZIGG6ZwvQ74NPAe4JbMfCAiHgTeEhF7gPvpXGx+dM02SeNlL4kC4xgUQ9N3OBwmhraVEEcJMUiSOjb1bAslWEfhmpmnV/9/H1jomfYUsK3nT+q2SRove0lIkiRpqq15V2FJU+864F10bth0S2Y+ANwInFz1fniMTu+Hum2SJElSq4bpKixpCthLQpIkSbPCM66SJE1Yz9jLQ4+z7NjLkqRZZeEqSdIEVWMv30dnnGSAy+iMn3w2cELVPkqbJElTz8JVkqQJyswnM/MsYH/VtBX4QvV4efzkUdokSZp6XuMqSVJZRhlneaixl0sax3YcsXSPj1x32aW8J6XEAeXEUkocktpl4SpJUllGGWd5qLGXSxrHdhyxdI+PXHds5FLek1LigHJiKSUOSe2yq7AkSWW5k874ydDp+nv3iG2SJE09C1dJksoyyjjLjr0sSZpJdhUWAJu6ulEB7PvQRROKRJLmU9fYy6OMs+zYyy3o3ma6vZSkdnjGVZIkSZJUNAtXSZIkSVLRLFwlSZIkSUWzcJUkSZIkFc3CVZIkSRpBRPx2RHwpIj4fES+qHu+NiA9V00+q0yZpMAtXSZIkaUgR8TLglZn5WuDzwJ8Cu4GzgTdExMuB36zZJq3bpit3P/NvljkcjiRJKpbDtWkKvB44ISK+CDwCvBS4JjN/HBF/DZwHbAX+c422b09mFaTyWbhKkiRJw3sh8GhmvjEivgL898DBatrjwAuAE2u2HSYidgA7ADZu3Mji4iJLS0ssLi6OYVXWp18cOzcfWvF8UJzd8621jLWWNSiWtV6rbqx17dx8iI3Hdv4fdlmrvS/r/ZuSvyfDsHCVJEmShvc48K3q8T8DLwI2VM83AA8BB2q2HSYzdwG7ALZs2ZILCwssLi6ysLDQ7FoMoV8c23t7SVy6cnq/+Xrn6V3GWssaFMtar1U31rq2X7mbnZsPcc3eI4de1mrvy3r/puTvyTAsXAsz7i5Rs973XZIkqWX3AVdUj0+nU8ReEBF/B7wOuBY4pWbbVGpr/9JLB+abhaskSZI0pMz8SkQciIi/Af6BThF7C3Ap8LnM/KeI+EidtgmtQms8gaJRWLhKkiQ1wLNB8yszf62n6bU90w/UaZM0mIWrJE2h7h1kd44lSdKscxxXSZIkSVLRLFwlSZIkSUWrVbhGxFER8bnq8TERcWtE3B8RN0TH0G3jXT1JkqZPRCxExD3Vv4cj4p0Rsb+r7Qy3s5KkebLmNa4RcSzwNeDlVdNlwP7M3BYRtwLnA6eO0HZ742slSZo6Xrf7rMxcBF4DEBG76YzxeH1mXrU8T0RcjttZrYM5JmmarXnGNTOfzMyzgP1V01bgC9Xju4DzRmyT1DB7SUizISJ+gs64kN8GLomIr0fEzVUuup2VJM2NYe4qfCJwsHr8OHDGiG0rRMQOYAfAxo0bWVxcHBjI0tLSqtPb0mQcOzcfWvF8PcutE0fv8gcZdn1m8TOZtjjsJVGm3mEyPnnhcROKRFPmfOBO4EHgA5m5OyLuBV5Hg9vZUn4z4fDfzVG2i/2WUffvV3tPBi2viVjXE8d6DfM+jCuWUZQSh+px7FY1ZZjC9QCdLktU/x8Ajh+hbYXM3AXsAtiyZUsuLCwMDGRxcZHVprelyTi2944Bd2n95daJo3f5g6znddcbQxvmOY7MfBI4KyKWBzLfCtxcPV4+A3PaCG0WrlJ7LgY+AzwG3FG17QNeRP3t8Qr9trOl/GbC4b+bo2wX+y2j7t+v9p4MWl4Tsa4njvUa5n0YVyyjKCUOSe0apnC9E7iAzs7sVuDDdM7KDNsmabzsJVFAHL1nYgbFUPeMzahnTlaLoW3LcTSxTqPGUJKqO/B5wHuADwDfjogbgDOBP6BTpLqdlSTNhWEK1xuBt0TEHuB+OoXs0SO0SRove0lMKI6V3aNW/tx+8sLj+sZQ94zNqGdOoLzPo4l1GjWGwrwa+GZm/ltEXAd8mk4Re0tmPhARD+J2VpI0J2oXrpl5evX/U8C2nsmjtGnG9V7b4J0MW2cvCWkKZebXgTdWj78PLPRMdzsrSZobw5xxlTRd7CUhaeZ5kLQ/b4wjaVZYuEozyl4SkiRJ4+XBofZYuEqSJEnSjOsusqexV4qFqySpVdO+4ZQkSe2zcJ0Qd9wkSZIkqR4LV0nSxGy6cjc7Nx86bBggSZKmmTeMa56FqyRJKoo3O5Ek9Tpi0gFIkiRJ0y4ifisi7oiIkyLiSxGxNyI+VE2r1abh7f3eQTZdudsDXzPMM64FMMEkSZoM7zmhJkTEacB24FHgN4HdwB8BfxcR/wfwjjptmfntCYSvdZrEvrtdjy1cJWnd3NGVJPW4Fng/cAWwFfjPmfnjiPhr4Lx1tFm46hme3FrJwlWSpoQbMEkqT0S8HbgfeKBqOhE4WD1+HHjBOtr6LX8HsANg48aNLC4usrS0xOLiYrMrMoTlOHZuPjTScj5642dXPN+5ud7fdb8HG4/lmTh635vu+Lqn9cbdPW3v9w4+83jzyRsGLq9XdxyDlt2t+3U6yx646IHLG7R+vd+TQfONW1PfVwvXwnlmR5IkqWjbgFOBXwDOAH4MLFc6G4CHgAM12w6TmbuAXQBbtmzJhYUFFhcXWVhYaHxF1qNzV/gfcc09TzCpkmLfpQvPPP7ojZ/lmr1HHtYOrLhzffe03jvaD5q22vJ67dx86Jk4Bi277rJWUyfW3u/Jaus0Tk19X705kyRJkjSkzHx7Zr4GeBtwH/Ax4IKIOAJ4HXA3cGfNNkkDeMZVkiRpytgjq2gfAW4BLgU+l5n/FBG12iYWsTQFLFwlSSpIRFwIfALYVzX9OnAVcAqwh86dSJ8D3LRWW2Zmm7Fr9nQXyDs3H2JhcqEULzP3AT9fPX1tz7QDddokDWbhKklzau/3Dj5zvYtnbIpzfWZeBRARlwP7M3NbRNwKnE/nero6bbdPKP51Wy6QOjcPGX33ZNDNzLzJmVSfZ/ZVEgtXSZLKc0lEvAl4GHiazplUgLvoDJlxGnBzjbapKVxL44EdqXwrewRMMBC1wsJVkqSyPAh8IDN3R8S9wKuAP6+mPU7nrqW9w2gMajtMqUNrLA/TMGgoiWWD4hx1OI7eZTc9tMZqVht2Y9BnU3dYi0HvS93Yuv9+47HtDqExSAnfV0nts3CdcXaJkobTmzuecVGLHgPuqB7vA36alUNmHACOr9l2mFKH1tje1VW431ASy7qHcFiZp6Pv0oxzaI3VrDZExaDPpu6wFoOG2hgmtp2bD/HWCX9PoLmhNaRpNo/7+A6HI0lSWa4A3lYNkXEmsBO4oJq2la5hNGq0SZI0EzzjKmmudAZMP+S1ayrZdcCngffQGSrjz4GbI2IPcD+dAvVo4C012iRJhZnHs6VNsHDVmryjnCS1JzO/D4eNOrKt5/lTNdtmjjt8kjTYLA9hZeEqSSPwWlhJkmaDB8bKZuEqSZJUiN4d52EubXDnW9IssnCdInbZlSRpflmQSppnFq7qy42j5oUHhCTJ30Jp3Ny3Ht1QhWtEXAh8gs74cgC/DlwFnALsAd4BPAe4aa22zMzhw9c0cuPYLvN19nmdrSRJmnWjnHG9PjOvAoiIy4H9mbktIm4FzgdOrdl2+2irIKkG81WSJA3NM4aatFEK10si4k3Aw8DTdM7MANwFnAecBtxco23FjnBE7AB2AGzcuJHFxcWBASwtLa06vS3DxLFz86GRXvOjN352xfPNJ2/oG8eor9NrrfVcbwzj+vym+bsxJmPJV0maJSuHkZhgIJKkwwxbuD4IfCAzd0fEvcCr6AyQDvA4cAZwInCwRtsKmbkL2AWwZcuWXFhYGBjE4uIiq01vyzBxbG/4qNW+Sxf6xjGO11nNemNYa3nDGuW70WRX5kK+o2PLV5i+g007Nx9i47H9D6h0x9Y7fbVpw8z3g8cOPnMAavPJG0Z+3UF/s5ru96HNz6V3Pfp9Hm1/T0r4bs4DLxWRJA1r2ML1MeCO6vE+4KeB5T2vDcAB4PiabZLGa6z5Om0Hm7ZfuZudmw9xzd7Df/66D6T0HnBZbdow83XH0MTrDvqb1Xz0xs/2jWHcetej3+fRZjxQxndTkiQNNmzhegXw7Yi4ATgT2AlcQKdb4Vbgw3Suj6vTJmm8zNcp5jVF0uwpPa9Lj09qmzlRhmEL1+uATwPvAW6h0+3w5ojYA9wP3AkcDbylRptmkAleFPNVkiTNDfdDZ9NQhWtmfh9Y6Gne1vP8qZptasCmqvvj9it3e92QVjBf59tqG29vPiNpFF6z3BERAXySzr0gfgC8HfhLHHauSBa102uUuwpLknq4QZSkuXMucGRmnhMRi8C7cdg5qXEWrpIkSarFg3N9PQJcWz1+Gvg94Feq5w47JzXEwlWSpIL06Xa4C/g4nbuCA/wy8BB2O5SKkJnfAYiIN9O5P8R91Btibuhh59oawmutYdgGDS83CaXEUkoc0Ill0LB6w3x/9n7v4Irn3cP5raap76uFqyRJZentdvhj4PrMvGp5hoi4HLsdFsEzkAKIiDcC7wUupnOgaazDzrU1hNdaw7ANGl5uEkqJpZQ4oBPLW7u+J92f5zDDzq02ZN9qmvq+lvGuSpKkZb3dDgEuiYg3AQ8Dv0hniCq7HbZk2OLUmxfNh4h4MfA+4MLMfCIi7sRh5zRlpuH3ysK1RbN4VHYavuSSNE36dDt8EPhAZu6OiHuB1zED3Q57X295Wind7JqO46M3fvaZx+u5o3edOAa9l3XjqRvTarG08f1Z1tb3dR3eCbwEuK3T058bgJMddk5qloVrw0oo5CZVIHcPydPU8pZZFKsNq+XOLB54apo525yebodH8+z1rfuAF9HpUjjV3Q57u5gtTyulm900xTHovWwzlmG6HQ6rre9rXZl5NXB1T/Of9Tx32DlpRJP/RZakOdd0UWyRPd36dDu8Cvh2RNwAnAn8AZ0i1W6HAsx5SfPBwlVahWeQpOGYOyPp7Xb4eeBdwHuAWzLzgYh4ELsdSpLmiIWrJsqdW0laaUC3w6t65pn6boeeJZSkMpW6f27hqmK4EyOVoTcXS9poSZKk+mZp/9rCVXOj1KNHUunMHUmSZss0FrQWrpp705i40qRYxEpqkr8pkuqycB0jCyJJkjQPvMRAmj9tH3g6YuyvIEmSJEnSCDzjqrnk2XBJksbHLsCSmuYZV0mSJElS0TzjKkmyF4IkSSqahesq7OZyOHduJUnSsFbbj3C/S9JqLFwlSZI0Nh70ltQEC9cG7P3eQbb7ozxXvO2/5M6oJEmzrqRt/dwXrsN0S+n9AHdubjQk9dHE5yRpMsxFSZoe/marVHNfuGr6tXVNjD/k08vPTpIkabpZuHZx53b6LX+GOzcfwq+3NL3sjj873LZKkprgnn1NbnjL4WchSZIklWO1/fNPXnhcI6/RauEaEccANwGnAHuAd2RmthmDpPrMWWm6mLOaFfPS68Kclepr+4zrZcD+zNwWEbcC5wO3D7uw3rv5dv+oeVZObZrhseday9mm+RugOdVozkql6P5Nb+rsTSEmnrNuLzUt2i5ctwI3V4/vAs7DDapUsonkbN0j7W5s50fTB4dm+GBTozlrLkpjN7acXe23zZzVNIo2eyNExG3AH2fmHRFxOfDqzPzVnnl2ADuqp2cA31plkScBB8YS7PoYR1kxQNlxnJaZL5xEMOtlzhpDS0qIY7UY5iFnS/gMlpUSi3EcrpRY1orDnG1PKXFAObGUEgeUE0sjOdv2GdcDwIbq8Qb6rEBm7gJ21VlYRHwjM7c0F95wjKOsGIyjUeasMcxFHCXE0JChcrak9S8lFuM4XCmxlBJHQ6Y6Z0uJA8qJpZQ4oJxYmorjiCaCWYc7gQuqx1uBu1t+fUnrY85K08WclaaLOSvV1HbheiNwckTsAR6jk6ySymXOStPFnJWmizkr1dRqV+HMfArY1uAia3VPbIFxPKuEGMA4GmHOjpUxPKuEOEqIYWQj5GxJ619KLMZxuFJiKSWOkc1AzpYSB5QTSylxQDmxNBJHqzdnkiRJkiRpvdruKixJkiRJ0roUX7hGxDERcWtE3B8RN0REDDNPS3FERHwqIr4aEX8VEY13xV7PukbEb0XEHZOKISJ+OyK+FBGfj4ijJxFHRBwXEZ+NiC9HxB81HUPX6xwVEZ8bJdZZUULOmq/rj2Gc+WqulqGE3FxHHGPPz7qxdM07lhxdTxzztF3tej1z1pxdVyxd85qztJuz487X4gtX4DJgf2aeDZwAnD/kPG3EcS5wZGaeAzyPZ+8S13YcRMRpwPYxvH6tGCLiZcArM/O1wOeBl04iDuBS4KuZeS7wyoh4RdNBRMSxwH0DXn89sc6KEnLWfF1HDC3kq7lahhJys+5rtJGfdWMZd47WimOetqvLzFlzdshYzNmVWsnZNvJ1GgrXrcAXqsd3AecNOU8bcTwCXFs9fnoMMdSNgyqO908whtcDJ0TEF4HXAt+dUBxPAT9RHdE5hjF8Lpn5ZGaeBexfZbY2vqOlKCFnzdf1xTDufDVXy1BCbtZ9jTbys24sMN4crRvH3GxXl5mz5uyQsYA5262VnG0jX6ehcD0ROFg9fhx4wZDzjD2OzPxOZn49It4MHA3cNok4IuLtwP3AA2N4/VoxAC8EHs3Mn6NzhOk1E4rjL4A3AP8A/GNmPjiGOOpo4ztaihJy1nxdRwyMP1/N1TKUkJu1XqOl/KwVSws5WisO3K4OYs6asyuYs4cpKWdH+r5OQ+F6ANhQPd5QPR9mnjbiICLeCLwXuDgzfzShOLbROcrzl8CrIuI9E4jhceBb1eN/Bk5uOIa6cbwf+Hhm/hTwgoj42THEUUcb39FSlJCz5uv6Yhh3vpqrZSghN2u/Rgv5WTeWcedo3TjcrvZnzpqzvczZlUrK2ZG+r9NQuN7Js/3ktwJ3DznP2OOIiBcD7wMuyswfjiGGWnFk5tsz8zXA24D7MvO6tmOg08f91dXj0+md5zviAAAgAElEQVQkbNPqxPFc4N+qx08Bx48hjjra+I6WooScNV/XEQPjz1dztQwl5Gat12gpP2vF0kKO1ooDt6uDmLPm7Arm7GFKytmRvq/TULjeCJwcEXuAx4AHI+JP1pjnzgnF8U7gJcBtEXFPRLx7QnGM25oxZOZXgAMR8TfAtzLz65OIA/gY8GsR8RXgWMbz3VghIn5yQt/RUpSQs+brOmJoIV/N1TKUkJt142gjP+vG0oYS8rRWHEwgV5eZs+ZszVjaYM6uYRz5GpnZVHySJEmSJDVuGs64SpIkSZLmmIWrJEmSJKloFq6SJEmSpKJZuEqSJEmSimbhKkmSJEkqmoWrJEmSJKloFq6SJEmSpKJZuEqSJEmSimbhKkmSJEkqmoWrJEmSJKloFq6qJSJeFhH39rSdHRF/HxHfj4jfnlRskg7XL2e7pv1+RHy17ZgkrS4izoiIb0TEDyLi+oiIScckaTD3hdtl4aoVImJfRGzqafsgsBd4Wc/s1wP/C/BTwG/1/p2k8VtnzhIRJwG/2Upwkgbql7vAnwJ/DmwCtgJvaDcqSYMMyFn3hVtk4ao1ZebvA6/sbouII4CHM/OLmXkQeIjOhlbShPXL2S6/A3ytxXAk1XcOcFNm/ivwt8CZE45H0gDuC7fPwnVGRMSlEfHdiHgkIt5XtV1WHR16KCK2V20LEbEYEddExL9ExBcj4tiI+I2IOACcAvxtRByIiOMGvV5m/jgz/6dqma8ATge+OfYVlWZE2zlbLeslwFuBa8e8etLMGnPu/lRmPlp1ET6Lzo6wpBGMK2fdF26fhesMqJLlD4Gfo3N09oqIOAu4Gngd8Brgv0TE5upPzgEeBl4MPB94Q2Z+JDNPqtp/JjNPyswnarz2FuAB4FOZ+WjDqybNpAnm7O8CHwWWml4naR6MO3cz85Hq734HeBL4TDtrJs2mNra37gu3x8J1Nvw8sDszH64S5qV0kvFzmflQZj5MZ+N3QTX/o8C1mfkU8HfA84Z94cz8RvV6l0TE1lFWQpojredsRJwKXAR8rIkVkObU2HM3In4VuBy4KDP/fRwrIc2Rsees+8LtsXCdHd13Hnw9h3+22TXPdzMzu9rX/2IRp0fERwEy83vAV4EtwyxLmlOt5izwWuAE4Lt0NtI/ExGfHXJZ0jwbW+5GxPnAlcDru86+ShrNWHLWfeH2WbjOhruAiyLilIh4Pp0zKt8AtkXEqRFxMvAW4LZq/h+vsqwDwE9GxBERceIa8729Strn0knUvx95TaT50HrOZuaNmfn8zHxxtey/zcw3NbI20vwYW+5W17X+MfBLmem1rVIzxrm9dV+4ZRauMyAzv0nn2rUv0UmY/5qZX6Zz1PaLwL3ABzNzb43FfRD4JPDfeLbbRL/X/P/o3P77DuA7wGcy8/8ZYTWkuTGJnJU0ujHn7iuBzcCt1c1fDkTE9c2vhTQ/xpmz7gu3L549Gy5JkiRJUnk84ypJkiRJKpqFqyRJkiSpaBaukiRJkqSiWbhKkiRJkopm4SpJkiRJKtqRkw5gNSeddFJu2rRp4PQnnniC4447rr2ACo6jhBiMo14M991334HMfOEEQhq7tXK2nxI+qya4HmVpcj3mOWdL/T6UGheUG1upcUHzsZmzZXzOxtKfsRyuds5mZrH/XvWqV+Vq7r777lWnt6WEOEqIIdM46sQAfCMLyK9x/FsrZ9fzPk0b16MsTa7HPOdsqd+HUuPKLDe2UuPKbD42c7YMxtKfsRyubs7aVViSJEmSVDQLV0mSJElS0SxcJUmSJElFs3CVJEmSJBWt6LsKCzZdufuZx/s+dNEEI5G0zLyUVJLu3yTwd0maFPcPxsszrpIkSZKkolm4SpIkSZKKZuEqSdKERMenIuKrEfFXEXF8RNwaEfdHxA3V9GOGbZv0+kmS1BQLV0mSJudc4MjMPAd4HvBuYH9mng2cAJwPXDZCmyRJM8GbM0mSNDmPANdWj58Gfg/4ler5XcB5wGnAzUO23T6+0DVtvHGMpGlm4SpJ0oRk5ncAIuLNwNHAfcDBavLjwBnAiSO0HSYidgA7ADZu3Mji4uLA+JaWlladPimlxgWTiW3n5kMrnvd7/aWlJXZu/tGq80xKyZ+npHJYuEqSZtK0nF2KiDcC7wUuBj4ObKgmbQAOAMeP0HaYzNwF7ALYsmVLLiwsDIxtcXGR1aZPSqlxwWRi2947HM6lh7/+4uIi19zzxKrzTErJn6ekcniNqyRJExIRLwbeB1yUmT8E7gQuqCZvBe4esU2SpJlg4SpJ0uS8E3gJcFtE3AMcBZwcEXuAx+gUozeO0CZpDCLiqIj4XPX4wojYHxH3VP/O8G7gUvPsKixJ0oRk5tXA1T3Nf9bz/Clg25BtkhoWEccCXwNe3tV8fWZe1TXP5XTu8r0tIm6lc5fvU2u2eVM1qQ8LV0nFm5ZrFSVJsy8znwTOioh/6mq+JCLeBDwM/CKd7vqN3A18Wm+oNo+xdN8obdDrzeP70hQLV0mSJGl4DwIfyMzdEXEv8DoavBv4tN5QbR5j6b5R2qAboM3j+9KUNQvXqq/9J+kk0g+AtwN/CZwC7AHeATwHuGmYtszMJldIkiRpWtnDZCo9BtxRPd4HvIjOXb0buxu4pHo3ZzoXODIzzwGeB7ybTl/8s4ET6PTFv2yENkmSJGlaXQG8LSKOAM4E/h7vBi41rk7h+ghwbfX4aeD3gC9Uz5f74m8doU2SJEmaVtcB76Jzw6ZbMvMBvBu41Lg1uwpn5ncAIuLNwNHAfQzfZ3/NfvzTeAH6OOOoc5H3uGNYD+MoKwZJkjQemXl69f/3gYWead4NXGpYrZszRcQbgfcCFwMfZ/g++2v245/GC9DHGUedi7zHHcN6GEcZMUTEUcBnMvPiiDiGIa9B79fmdemSJElq25pdhSPixcD7gIsy84eM1mfffvzSmFXjy93Hs9eQj3INutelS5IkaeLqXOP6TuAlwG0RcQ9wFMP32bcfvzRmmflkZp4F7K+aRrkG3evSJUmSNHF1rnG9Gri6p/nPep7bj38E3vpeYzbKNeiNXpfeT51rgete692WfvHMyjXNs7QeOzf/6Jnns7BOkiTNs1rXuEqaaqOMJdfoden91LkWuO613m3pF08J11U3YZbW45p7nnjmeQnfG2mWdR+EBw/ES2pena7CkqbbXF6XvunK3c/8kyRJ0nSzcJVm3yjXoHtduiRJkibOrsLSjOoaX26Ua9Bbuy7da70lzRp/1ySpOZ5xlSRJkiQVzcJVkiRJklQ0C1dJkiRJUtG8xlWSNPMcqkOSpOnmGVdJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklQ0b84kSX14Mx+1KSKOAj6TmRdHxIXAJ4B91eRfBh4CbgJOAfYA7wCeU6ctM7O9NZFW52+rpGFZuEqSJqZ7J3Zed2Aj4ljga8DLu5qvz8yruua5HNifmdsi4lbgfODUmm23t7UukiSNi12FJUmaoMx8MjPPAvZ3NV8SEV+PiJsjIoCtwBeqaXcB562jTZKkqecZV0mSyvIg8IHM3B0R9wKvA04EDlbTHwfOWEfbChGxA9gBsHHjRhYXFwcGsrS0tOr0SSk1LlgZ287Nh55prxvvqH8z6O+WlpbYuflH6152XXViGDRfyZ+npHJYuEqSVJbHgDuqx/uAFwEHgA1V24bq+fE121bIzF3ALoAtW7bkwsLCwEAWFxdZbfqklBoXrIxte3dX+EsXav39qH8z6O8WFxe55p4n1r3suurEMGi+kj9PSeWwcJUkqSxXAN+OiBuAM4E/oFOQXgDcTKc78IfpXM9ap00amjdTklQKr3GVJKks1wHvonPDplsy8wHgRuDkiNhD54zsnetokyRp6nnGVZKkAmTm6dX/3wcWeqY9BWzr+ZO6bRqRZx0lafJqnXGNiKMi4nPV4wsjYn9E3FP9OyMijomIWyPi/oi4ITpqtY139SRJkiRJ027NM66OL9eu3qO6kiRJkjTv1jzj6vhykiRJkqRJGuYaV8eXaziO3jHNBpmH92KW4ighhmURsUDnzqQApwH/K3AVnaE2AH4ZeAi4CTgF2AO8A3hOb1tmZltxS5IkSTBc4er4cg3H0Tum2SCrjbk2K+/FLMVRQgzLMnMReA1AROymk3/Ddvm3e/8MWr5MYefmQyvvCiRJklSAYQpXx5eTplRE/ARwOvBR4H+PiDcBDwO/SCcnb65mXe7Kf1qfttt7llm7l0Q/y2emu3se9C5jtWmDDPM3g/6+dxn9ll3SGfZhLK/TxmOHe79GfV1o9nWXlpbYuflHA6dP82clSdI8GqZwvQ74NPAeqvHlIuJB4C3VuHH30xk37uiabZLacz6dvBuly/8K6+kl0c/ymenunge9vQtWmzbIMH8z6O97l9Fv2SWdYR/G9q4zrm9tcT1G/ZwGWVxc5Jp7nhg4vcnXkjR/IuIo4DOZeXFEHEONS23qtnlJjtRf7cLV8eWkmXAx8BlG6/IvSdLc6jPixmUMP7qGl+RINdUax1XS9KvuAH4enS6/VwBvi4gj6HT5/3s6Z2IvqGbfCtw9oE2SpLnVZ8SNUUbXcMQNqaZhugpLmk6vBr6Zmf8WEaN0+ZckSc8aZXSNmRxxA+Yzljr3bZjH96UpFq7SnMjMrwNvrB6P0uVfkiQ9a5TRNWZyxA2Yz1jq3LdhHt+XpthVWJIkSRpe3UttvCRHGoGFqyRJkjS8G4GTq8tqHqNTjI7SJqkPuwpLkiRJ69Q14sYoo2t4SY5Uk4WrJGlmbOoaj9ZNnCRJs8OtuiRJkiR12dR1o6Ve+z50UYuRaJmFqyRJmlvdO6fujEpSubw5kyRJkiSpaJ5xlTQzVuvWI0mSpOnlGVdJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzWtcJUmasIg4CvhMZl4cEccANwGnAHuAdwDPGbYtM7PdtZGk2ebdyCfDM66SJE1QRBwL3AecXzVdBuzPzLOBE6r2UdokSZp6Fq6SJE1QZj6ZmWcB+6umrcAXqsd3AeeN2CZJ0tSzq7AkSWU5EThYPX4cOGPEthUiYgewA2Djxo0sLi4ODGRpaWnV6ZPSZFw7Nx965vGgZXbPs9p8sDK2OsseJp5h4ltaWmLn5h+NZdmjzlfq90zNs4utRmHhKqk4jseqOXcA2FA93lA9P36EthUycxewC2DLli25sLAwMJDFxUVWmz4pTca1vXtH+tL+y9ze85s0aD5YGVudZQ8TzzDxLS4ucs09T4xl2aPOV+r3TFJZLFwnxB1zSdIAdwIXADfT6fr7YeDUEdokSZp6tQpX73aocbHLiCQd5kbgLRGxB7ifTiF79AhtkjQTek/8TOu+o/u/w1mzcK3udvg14OVV0/IdC7dFxK107lh46ghttze+VpKeEREXAp8A9lVNvw5cxZQeaPLHXrMqM0+v/n8K2NYzeZS2ueRvhSTNljUL18x8EjgrIv6patpKpwsSPHvHwtNGaFtRuE7jTSOGiaP35gR1zOJ7McxNKMYRxziUEEOX6zPzKoCIuBwPNEmSJGmKDHON61jvdjiNN40YJo7emxPUUfdmEJO03jiGuQnFOOIYhxJi6HJJRLwJeBh4ms6ZVBjxQJMkSZLGb1a6SY9imMJ1rHc7lNS4B4EPZObuiLgXeBXw59W0kQ40wfp6SfSzfGa6iV4Ig5YxzJnv1YZ26NdToLAz7Ou2vE4bj22290Pd14VmXrd7PVb7Tk3zZyVJ0jwapnD1bofSdHkMuKN6vA/4aRo80LSeXhL9LJ+ZbqIXwqBlDHM2f7WhHfr1FCjsDPu6La/Tzs2HeGuL69F0r4vu9bhm7+BNXJM9PCRJ08mzmNPliCH+5kbg5OqOhY/RKWRHaZM0XlcAb4uII4AzgZ10DiBB5wDS3Tx7QGqtNmlsNl25+5l/kiRJ3WqfcfVuh6NzZ0wTch3waeA9wC10ugnfPE/Danh3UfXyOyFJ0nQZpquwpCmSmd8HFnqaPdAkSZLUkk1dl7JYgg1nmK7CkiRJkiS1xnJfkip255ckSU1wn6J5nnFVYzZduZu93ztookqSJElqlIWrJEmSJKlodhWWJDVu1Lv2OraeJEnq5hlXSZIkSVLRPOMqSZKkiXN85dnkvU/UFM+4SpKkqeGNAFWaiLgwIvZHxD3Vv7Mj4taIuD8iboiOY+q0TXpd1L5NV+5e8U+DecZVkkbQPaD49it3e5ZAkubT9Zl5FUBEXA7sz8xtEXErcD5was222ycU/0SVcl+DaSoc57GHgoWrJE3YvG18pmnHQJJquiQi3gQ8DDwN3FS13wWcB5wG3FyjbS4LV82WcR2IsHCVJEmShvcg8IHM3B0R9wKvAv68mvY4cAZwInCwRtthImIHsANg48aNLC4uDgxkaWlp1eltWo5l5+ZDfad3x9k7z6B1qDvfoFjWWt4wVluPfjYeu/p8dde9zt+sZVzfl2E/p7VYuE6ReTsrI5XEs4SSpAEeA+6oHu8DfhrYUD3fABwAjq/ZdpjM3AXsAtiyZUsuLCwMDGRxcZHVprdpOZbtg7afe5/oerKyJNl36ULfP+ld1qD5BsWy1vKG0R1DneXt3HyIa/YOLsHqrnudv1nLuL4vw35Oa7FwVessACRJ0gy5Avh2RNwAnAnsBC6g0w14K/BhOtez1mmTNIB3FZYkSZKGdx3wLuBrwC10ugmfHBF76JyNvRO4sWabpAE84ypJkiQNKTO/Dyz0NG/ref5UzTZpbKb9skMLV0kqSClDApRs2je8kiRp/ewqLElSQSLiwojYHxH3VP/OjohbI+L+iLghOo6p0zbpdZEkqSmecdXUaeKMlGdsJBXu+sy8CiAiLgf2Z+a2iLgVOJ/OTV3qtE3lmJD+RkuSNzTtNVThGhEXAp+gc8tvgF8HrgJOAfYA7wCeQ2fw5VXbMjOHD1+SpJl0SUS8CXgYeJrOthPgLuA84DQ6dyJdq+2wwnUaxoTsHgOw9/V3bj70zDiIq8W22jLWO996xiTsfs/qxrDeeIaJb2lpiZ2bfzSWZY86X7/xPksZi1TTqbfg++SFx00oEjVplDOuc300WOPnEffmVF0GP0lncPMf0BkP7uM8e/Dpl4GH8GCTVIIHgQ9k5u6IuBd4FZ27lAI8TiePTwQO1mg7zDSMCdk9BmDv+H/br9z9zDiIq40NuNoy1jvfesYk7H7P6saw3niGiW9xcZFr7nli1XmGXfao8/Ub77OpcR8lzY5RCtexHQ2WmuBNblY4FzgyM8+JiEXgx3QdfIJ1HYAyZ6Xxegy4o3q8D/hpYEP1fANwADi+ZpskaY7M8v7vsIXr2I4GT0MXprpx7P3ewRXPd25u7jV7X6+E92KY7lvd+nUH6zdtmC5Rqy1jXO9bCZ9Jl0eAa6vHT1f/dx98+kU6g597sEmavCuAb0fEDcCZwE7gAjq5uBX4MJ2DSnXaJGns7CWnNgxbuI7taPA0dGGqG0dvd5gm9XahKeG9GKb7Vrd+3cH6TRumS9RqyxhXd6QSPpNlmfkdgIh4M3A0hx98eh0tHGzqp9+1TXWtdrBjkI/e+NkVzzefvOGZx8PEsKzOQZtBVnvdtg5+LMew8dhmXnOtaxTXq+6Bp+71qPs6BR1gWnYd8GngPcAtdA4M3xwRe4D7gTvp5PFbarRJUquWi9idmw+NdV94WN7waHoNW7h6NFiaMhHxRuC9wMV0dnD3VZP2AS+icyBprAeb+ul3bVNdqx3sGGYZo2xg6xy0GWS1123rOq/tXTsab23ggMta1yiuV90DT93rcc3eepu40q6ly8zvAws9zdt6nj9Vs02SNEUsrAcbtnD1aLBqMwEnLyJeDLwPuDAzn4iIq1h58OkP6BSpHmySJEmHmeVrJzUdhipcPRosTZ13Ai8BbuvcYJjPA++iOviUmQ9ExIN4sElj4MErSZI0qlHuKiwVp/u6Cr/ez8rMq4Gre5qv6pnHg00N8AYVkiSpTfNygPiISQcgSZIkSdJqPCUlaWI2VXeiHvamSKUfYWzi7KtncCVJ82y1bb3byPli4SpJklrnjV4kSeth4aq5UfrZOUmSJEn9WbiqGBaWs8/PWJIkqT2ztO9l4aqRtJUMda9vkCRJ0nRz3079WLhqLPzBkSRJktQUC9eGWbBJUnv8zZUkaT5YuGpd3EmUJEmS1DYLV0lqgUN/aF45zqI0vTxhoZJYuE6p3h+ST1543IQimX4WFPNtFjfKTRcKFh6SJM2PUrf7Fq6SNMOG2fiUWMyXGJMkSWqPhesMKvUoiSRJkiQNw8K1AXu/d5Dtng2QJMmz49KcMNen26Yrd7Nz86FnaphpONll4TojLJ6l2WdvCkmS+rOQHs2g96+kfQ8L1zm2WoJP+ospqXze2EySpp+/5ZoWFq7qq6SjK22b53VXezwyrGnnb6UkaVkb+zUWrjWt9mHs3NxiIJI0Qav9FlqMzy8/e2l2mM8qlYXrjGvix2eef8A8oyBJkmbNPO/baXiT7lbeauEaEccANwGnAHuAd2RmthmDpPrM2XJ5UEX9mLPSdGkzZy1WNe3aPuN6GbA/M7dFxK3A+cDtwy6s9066o+68TfooglSgRnNW41F3Z8Sdlrkw1u2spMa5ndXUanu/ou3CdStwc/X4LuA8JpycXq+luub0DFdxOStpVeasZtIMb4MbzVkPNmmWRZs9iCLiNuCPM/OOiLgceHVm/mrPPDuAHdXTM4BvrbLIk4ADYwl2fUqIo4QYwDjqxHBaZr6w7WCGMYac7aeEz6oJrkdZmlyPec7ZUr8PpcYF5cZWalzQfGzmbBmMpT9jOVytnG37jOsBYEP1eAN93qjM3AXsqrOwiPhGZm5pLrzhlBBHCTEYR3kxNKDRnO1nRt4n16Mws7IeQ5jJ7WyvUuOCcmMrNS4oO7YWzGzOGkt/xjK8I1p+vTuBC6rHW4G7W359SetjzkrTxZyVpos5K9XUduF6I3ByROwBHqOTrJLKZc5K08WclaaLOSvV1GpX4cx8CtjW4CKH7p7YsBLiKCEGMI5uJcQwkjHkbD9T/z5VXI+yzMp6rMsMb2d7lRoXlBtbqXFB2bGN1YznrLH0ZyxDavXmTJIkSZIkrVfbXYUlSZIkSVqX4gvXiDgmIm6NiPsj4oaIiGHmaSmOiIhPRcRXI+KvIqLRrtjrWc+I+K2IuKPJ119vHBHx2xHxpYj4fEQc3XYMEXFcRHw2Ir4cEX/U5Ov3ea2jIuJzo8Q7y0rInyaUkoOjKiGHm1Da78C0KmU7O2RcE/ndKPW3oOTcNl+bUVq+lpSnJeVlSbk4S7lXfOEKXAbsz8yzgROA84ecp404zgWOzMxzgOfx7F3i2oyBiDgN2N7wa68rjoh4GfDKzHwt8HngpW3HAFwKfDUzzwVeGRGvaDgGACLiWOC+ATEsa+M7WrIS8qcJpeTgqErI4SYU8zsw5UrZzg7zmpP63Sj1t6Dk3DZfm1FavpaUpyXlZUm5ODO5Nw2F61bgC9Xju4DzhpynjTgeAa6tHj89oRioYnj/GF5/PXG8HjghIr4IvBb47gRieAr4ierI0jGM5zMhM5/MzLOA/avM1sZ3tGQl5E8TSsnBUZWQw00o5ndgypWynR3mNSf1u1Hqb0HJuW2+NqO0fC0pT0vKy5JycWZybxoK1xOBg9Xjx4EXDDnP2OPIzO9k5tcj4s3A0cBtbccQEW8H7gceaPi11xUH8ELg0cz8OTpHkF4zgRj+AngD8A/AP2bmgw3HsB5tfEdLVkL+NKGUHBxVCTnchGn7HShVKdvZdb/mBH83Sv0tKDm3zddmlJavJeVpSXlZUi7OTO5NQ+F6ANhQPd5QPR9mnjbiICLeCLwXuDgzfzSBGLbROYLzl8CrIuI9DcdQN47HgW9Vj/8ZOHkCMbwf+Hhm/hTwgoj42YZjWI82vqMlKyF/mlBKDo6qhBxuwrT9DpSqlO3sUK85od+NUn8LSs5t87UZpeVrSXlaUl6WlIszk3vTULjeybN94bcCdw85z9jjiIgXA+8DLsrMH04ihsx8e2a+BngbcF9mXjeJOOhc8/nq6vHpdBKy7RieC/xb9fgp4PiGY1iPNr6jJSshf5pQSg6OqoQcbsK0/Q6UqpTt7Lpfc4K/G6X+FpSc2+ZrM0rL15LytKS8LCkXZyb3pqFwvRE4OSL2AI8BD0bEn6wxz50TiuOdwEuA2yLinoh49wRiaMOacWTmV4ADEfE3wLcy8+ttxwB8DPi1iPgKcCzj+V4cJiJ+ckLf0ZKVkD9NKCUHR1VCDjeh2N+BKVPKdnaYuCb1u1Hqb0HJuW2+NqO0fC0pT0vKy5JycWZyLzJz0jFIkiRJkjTQNJxxlSRJkiTNMQtXSZIkSVLRLFwlSZIkSUWzcJUkSZIkFc3CVZIkSZJUNAtXSZIkSVLRLFwlSZIkSUWzcJUkSZIkFc3CVZIkSZJUNAtXSZIkSVLRLFw1lIj4/Yj46qTjkLS6iPjHiPiXiDhQ/XvDpGOS1F9EHBcRn61y9v+KiKMmHZOkwSJiX9f29V8j4vcmHdMss3DVQFUyburTfhLwm60HJGlVg3IWOCszT6r+fb7lsCT1MSBffwfYC7wIOAV4W8thSRqgX85m5qbl7SvwBeCfJxHbvDhy0gFoKv0O8DXguZMORFIt/zLpACTV8nbgf8jMf4+I7cChCccjqYaqd8R5wK9OOpZZ5hnXGRQRl0bEdyPikYh4X9V2WXWk6KFqY0hELETEYkRcU3VL+mJEHBsRvxERB+gc7f3bqvvDcdXfvAR4K3DthFZPmjnjzFngx8DN1fyfiIiYyEpKM2Jc+RoRz6FzpvX9EfEY8L8BD09oNaWZMeZt7LJzgX/OzP+31ZWbMxauMyYiXgH8IfBzwJnAFRFxFnA18DrgNcB/iYjN1Z+cQ2fD+GLg+cAbMvMjVZeHh4GfqbpAPFHN/7vAR4GlttZJmmUt5Ow/Ah8ETgN+ATi/nRa9Y3gAACAASURBVDWTZs+Y8/VE4DjgW9X8rwB+qbWVk2ZQC9vYZb8A3D72FZpzFq6z5+eB3Zn5cGY+CryUTmJ+LjMfysyHgc8AF1TzPwpcm5lPAX8HPG/QgiPiVOAi4GPjXAFpzowtZwEy8y2Z+bXMfBz4MnD2uFZEmgPjzNcf0dkv+z8z82ngNuDVY1oPaV6MdRvb5UI6OasxsnCdTd1dAV/P4Z9zds3z3czMrvbVvBY4AfgunST/mYj47IixShpTzkbEqcvdoirHAD8cJVBJY9vGPgr8K3B89fxI4N9HiFNSx7hytrPwiI3AfwfcM0qQWpuF6+y5C7goIk6JiOfTOTv6Df7/9u4/VrKzvvP8+2vZxo49NMZ2Lqhj3Im86WRx20poJDaYcLsTe5x1GwbISAh7wRC2o1HIkLhFZLQLWmnjXTPBQgzOkG0lI0dWT1AWxyK4h7Hxjxsw5kfsGXU3MDHgSbNuL+uh45XN9TpGNt/9o053V1dX3XtuVZ1Tz6l6v6RW133qVNWnfjzn1Pc5zzkFu6ovsZuBt3NiVOgna9zXUeBnI+K0iDg/M/dl5isy81XVffzHzHxrg89FWgSN9Vl6U/o/HBE/V93Pm4AHG3sm0vxrchv7k+p2vxURZ9Gb1u/PzkmTaXIbe8xVwN9Ue2nVIAvXOZOZ36J3HOqXgW8C/yYzvwLcBHwJeBj4aGYeqnF3HwVuB/6BE1MoJE1Rk302M58G/iXwN8BXgQ9n5mNTfxLSgmhhG/t79L5EPwn8LXDnNPNLi6al78Ue39qSOLE3XJIkSZKk8rjHVZIkSZJUNAtXSZIkSVLRLFwlSZIkSUWzcJUkSZI2ICLOiIjPV5cjIv48Ir4WEX8dEadHxNURcSQiHqr+bY2IsyLi7og4EBF3VLc7pW3Wz00qlYWrJEmSVFNEnA08Su8niwDeCJyemW8AXs6JM85+OjOvqP49BlwPHMnMy4HzqtsPa5M0xOmzDrCWCy64ILds2bKh2zz33HOcc845zQSaMrNOXxdyPvroo0cz88JZ52jCOH22SV34PKyly/nnKfsi9tlS3r8ScpSQwRwby9B0n83M54HLIuJ7VdNTwCeryz/uW/QdEfFW4AngN4GdnPiJoweAHcDFQ9pG/rTKLLezJbz3/cwzWklZYHp9tujCdcuWLTzyyCMbus3KygrLy8vNBJoys05fF3JGxPdnnaEp4/TZJnXh87CWLuefp+yL2GdLef9KyFFCBnNsLEPbfTYzv1s97tuAM4F7gJ8DPpKZ+yPiYeDNwPnAM9XNngW2jmg7SUTsBnYDLC0t8fGPf7y5J7OG1dVVzj333Jk89jDmGa2kLLB+nh07dtTqs0UXrpIkSVLpIuItwAeBazPzpYh4Grivuvow8NPAUWBT1bap+vvcIW0nycy9wF6A7du356wGDkoYtOhnntFKygLTy1PrGFcPQJckSZJOFRGvAj4EXJOZP6qabwTeGRGnAZcC3wTu58TxrzuBB0e0SRpi3cLVA9AlSZKkkd4DvBq4p9qB8z7gNuC9wNeBuzLz28A+YHNEHASeple0DmuTNMS6U4XbPgB9cB7/ysrKhp7Q6urqhm8zK2advq7klCRJ3ZaZl1T/fwz42JBFlgeWfwHYNbDMsDZJQ2z4GNemD0CfdB5/SXO6t9y0/6S/D99yzUl/l5R1PV3J2pWci2q9PiFJs9S/jnL9JM2OfVHDjHVypiYPQJckSZIkqV+tkzP18wB0SZIkSVKbNly44gHokiRJkqQW1Z4q7AHokiRJkqRZGGePqyRJkiRJrbFwlSRJkiQVzcJVmiMRcXVEHKmOP38oIi6PiLsj4kBE3BE9Z43bNuvnJ0mSpMVk4SrNn09n5hWZeQXweuBIZl4OnAdcCVw/QZskSVJrtty0//g/LbaxfsdVUtHeERFvBZ4Afgx8tmp/ANgBXAzcOWbbvU2HlyRJkgZZuErz5XHgI5m5PyIeBl4H/Fl13bPAVuB84Jkx204REbuB3QBLS0usrKyMDLdn24sn/b3WstOwurra+GM0qcv5zS5JkqbJwlWaL08D91WXDwO/BGyq/t4EHAXOnaDtFJm5F9gLsH379lxeXh4Z7oaBaT6Hrxu97DSsrKywVp7SdTm/2SVJ0jR5jKs0X24E3hkRpwGXAnuAq6rrdgIPAvdP0CZJc8Xj5ySpGyxcpflyG/Be4OvAXfSmCW+OiIP09sbeD+yboE2SJElqnVOFpTmSmT8Algeadw38/cIEbZIkSVLr3OMqSZIkSSqahaskSTMWEWdExOery2dFxN0RcSAi7oiesdtm/dwkSZoGC1dJkmYoIs4GHgWurJquB45k5uXAeVX7JG2SJHWehaskSTOUmc9n5mXAkappJ/DF6vIDwI4J2yRNmbMkpPZ5ciZJkspyPvBMdflZYOuEbSeJiN3AboClpSVWVlZOCbC6ujq0vW1t5Niz7cWh7cced5Fei67kmHWGapbE14Gfr5qOzXTYFRF305vp8JoJ2u5t+SlJnWDhKklSWY4Cm6rLm6q/z52g7SSZuRfYC7B9+/ZcXl4+JcDKygrD2tvWRo4bRvx+6+HrllvLUIc5ysmQmc8Dl0XE96qmncCd1eVjMx0unqDNwrWm/t9fPnzLNTNMojZYuEqSVJb7gavofZndCXyC3l6ZcdskNWvmsyTa0Oae7vVmQhzLs2fbS0Ovm4VZzwToV1IWmF6eWoVrRJwB/FVmXhsRZwGfBS4CDgLvBl42bltm5sTPQpKk+bEPeHtEHAQO0Ctkz5ygTVKzZj5Log1t7ulebybEsTy3PvTc0OtmYdYzAfqVlAWml2fdkzN5tkNJkpqXmZdU/7+Qmbsy87LM/B+yZ+y2WT8vaQEcmyUBvZkOD07YJmmIdQtXz3YoSZIkjbQP2FzNdHiaXjE6SZukIcY5xrXoefwlzekenJ8/mKukrOvpStau5JQkSd3WP0sC2DVw9SRtC2fLiKnBUr9xCtei5/GXNKd7cH7+4Nz7krKupytZu5JTkiRJUn3rThUewnn8kiRJkqTWjLPHtRNnO/R3nSRJkiRpPtQuXJ3HL0mSJEmahXGmCksqXET8fkTcFxEXRMSXI+JQRNxSXTd2myRJkjQLFq7SnImIi4Ebqj9/D9gPXA78RkT8/IRtkiRJUuvGOcZVUtk+CXwYuJHeSdB+NzN/EhF/w4nfUx637TszeD6SJEknncOm97OTljKLxHdbmiMR8S56Jz77dtU0+NvJr5ywbdhj1v7t5fV+23jauv67vl3Ob3ZJkjRNFq7SfNkFvAb4p8BW4Cec/NvJ3+fU32LeSNspNvLby+v9tvG0df13fbuc3+yaZ/5ygSS1z8K1o7YMFgBuOAVk5rsAImIL8KfAw8BVEfGfgDfTm0Z80QRtkrRwBre5kqT2WbhK8+1fA3cB1wGfz8zvRcTYbbN5CpI0PRahktRNFq7SHMrMw8CvV3++aeC6o+O2SZIkTYODSNoofw5HkiRJklQ0C1dJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklQ0C1dJkgoTEcsR8VD174mIeE9EHOlr2xoRZ0XE3RFxICLuiJ5T2mb9XCRJmgYLV0mSCpOZK5l5RWZeARwENgGfPtaWmY8B1wNHMvNy4DzgyhFtkiR1noWrJEmFioifAi4BvgO8IyK+ERF3VntSdwJfrBZ9ANgxok1Sg5whIbXj9FkHULcM/lj04VuumVESSVoIVwL3A48DH8nM/RHxMPBm4HzgmWq5Z4GtI9pOEhG7gd0AS0tLrKysnPKgq6urQ9vbNq0ch5585vjlPdvq3ebY466urrJn20vrLte0eXtPup6hX2auAFcARMR+TsyQuPnYMhHxfnqzIXZFxN30+vZrhrTd23Z+qSssXMdg8SZJasm1wF8BTwP3VW2HgZ8GjtL7gkz1/1Hg3CFtJ8nMvcBegO3bt+fy8vIpD7qyssKw9rZNK8cNA9vtOg5ft3w8w60PPbfuck2bt/ek6xmG6Zsh8Sngf4uItwJPAL9JbzbEndWix2ZDXDyk7ZTCtc5gUxumPWCwZ9uLE91+6eyT7+NT+z53/PK2zZuG3aRRJQ2olJQFppdnrMI1IpaBP6z+vBj4n4Gb6W1MAX4L+D7wWeAiesfnvBt42WBbZuZ40SVJml/VtMEdwAeAjwDfiYg7gEvpbYPPBa6i98V3J/AJentwBtsktWPqMySg3mBTG6Y9YDDOgFK/Pdte5NZDw0uZtgaU+pU0oFJSFphenrGOcfWkEZIkNe71wLcy8x+B24D3Al8H7srMbwP7gM0RcZDeHtn7R7RJase1wN3UnyExrE3SCBNNFW5iSsSk0yGO7Yrunzow7V3lg1MbRt3/estNstu8boZpGfa6tvG4G1Xa1AhJGldmfgN4S3X5B8DywPUvALsGbjasTVLDnCFRz+DhdtJGTHqM69SnREw6HeLYruj+6QfTni4wOLVh1P2vt9wku83rZpiWYa9rG4+7UaVNjZAkTVf/F1/PMaGCHJ8hERG3AX9Br4i9KzO/HRGPA2+vZkMcoPf9+cwhbZJGmLRwnfpJIyRJkqQucYaE1LyxC1enREiSJEkqjb8AMp/GOjlTxZNGSIWJiNMj4v+MiK9ExL+t+4Pn/jC6JEmSSjb2HlenREhF+mfAgcz85xHxBXozIur84HndNn8YXZIkSa2b9BhXqTMW5IQe/wH49xFxOvAK4Jepd3bvum0T/TD6rM6G3VVdzm92SZI0TRauhVuQYktTkpmrABHxdeAH1D+7dys/jD6rs2F3VZfzm12SJE2Thas0RyLifGAV+BV6e0kvod7Zveu2SZIkjeRvtaopk5ycSVJ59gD/PDNfAv4/4GZ6Z/KG3pm8H6R3UrRx2yRJkjply037j/9Td1m4SvPlj4H3RcRXgX8A/ox6Z/f2LOCSJEkqllOFpTmSmU/S2zvar87ZvT0LuCRJkopl4SpJkorl1D5J0+SJT7vLqcKSJEmSpKJ1eo/r4ChsV0dNHPmRJKmb3IZLi8d+PxudLlynwQ+eJElqmt83JGkyC1+4SpKk7puXWViSpOEsXGvy5BCSJEnS/HDAq1ssXCVJkgYc+0K7Z9uLdO3rktOSpfHYd8rWrTWxpqpLnbNLWSVJkiRN10IUros8DWCRn7skdVFEXA38KXC4avod4GbgIuAg8G7gZcBn12vLzGwzuyRJTfF3XCVJKs+nM/OKzLwCeD1wJDMvB84DrgSur9kmqWERcXVEHImIh6p/l0fE3RFxICLuiJ6z6rTN+rlIJVuIPa51uXdSklSId0TEW4EngB/T25MK8ACwA7gYuLNG271tBZYW3Kcz82aAiHg/vUGkXRFxN71BpNfUbLPPSiOMVbg6jUmSpMY8DnwkM/dHxMPA64A/q657FtgKnA88U6PtFBGxG9gNsLS0xMrKyinLrK6uDm1v2+rqKnu2vTTy+v6MvZMo1btuI5bOrn/7tV6z/vsY57XdyHsy6WNNK0dTSsgwhINNUsMm2ePqyFID/NkdSVp4TwP3VZcPA78EbKr+3gQcBc6t2XaKzNwL7AXYvn17Li8vn7LMysoKw9rbtrKywq0PPTfy+sPXLR+/fMPg9vNQ/+3G/7qzZ9uL3Hqo3u378wzqz7fWcqNs5D0Z9VjTONFhCZ+NEjIMmPlgUxvqDhhMMlC0ERsZVBrHRgeiShpQKSkLTC/PJIWrI0uSJE3fjcB3IuIO4FJgD3AVve3nTuAT9AaC67RJat7MB5vaUHfA4JRBpIZsZFBpHHUHoo4Nku3Z9hK/u2v0bdpU2uDOtPKM+243NrK0kVGlYdOCjlX0a43A1J0+NM5y6+XrVydr3TzjZNjIVKJRWdsazambda0RnSanTknSFN0G/AXwAeAuetvXOyPiIHAAuB84E3h7jTZJzXOwSWrBuIVrYyNLGxlVGhzROXzd8vGKfq3RnjWnFk243Hr5+tXJWjfPOBk2Mm1pVNZxpjuNo27WtUZ0Jp2m1RXVWQlvpzcw9F+BdwGfYYxj0Ie1eVy61KzM/AGwPNC8a+DvF2q2SWqeg01SC8YtXB1Zksr1RuD0zHxDRKwA72P8Y9A9Ll2SpqzNXzHw3BnNc7BpPtl3yjNu4erIksY2jZNDaE1PAZ+sLv8Y+F+A/7H6e6PHoHtcuiTNSP/2cs+2F0+pjCRpkYxVuDqyJJUrM78LEBFvozdY9Cj1jjefyXHpTSrtrHob1eX8ZpckSdPU3Km4JM1MRLwF+CBwLfAnjH8MeuPHpTeptLPqbVSX85tdGo/TEyVpuNNmHWCRbLlp//F/UlMi4lXAh4BrMvNH9KbkX1VdvRN4cMI2SZIkqVXucZXmz3uAVwP39E4wzB3A5jGPQfe4dEmtOzbA2zu8wK8qkiS3BtLcycyPAR8baP4/Bv6uewy6x6VLUuGcySVpEVi4SpIkTUGbP3MjlcTBE7XBwlWSJKkB0/4y78/JSWWzjzbLwlWSJEmSZsCZGvVZuEqSpJlzqqEkaS0WrnPIjb8kSWrCoSefOf6b3O4ZkkZzT+r0WbhKkiR1jIPUkhaNhaskSZKmypPUSKM58DQeC9c5YQeQJEmz4vcQSU07bdYBJEmSJElai3tcJUmSFpxTeyWVzsJVkiRphpxmK0nrs3CdgnkcpezSc+rPevvV58wwiSRJkqQmWLhKklSQiAjgdmAr8F+BvcCfAIerRX4L+D7wWeAi4CDwbuBlg22ZmS1GV4e4l1dS14xVuLpRlSSpMW8ETs/MN0TECvAT4NOZefOxBSLi/cCRzNwVEXcDVwKvGdJ2b/vx22Px1Ywuzboqgd+LVcek66vB2y9i3xz3rMLHN6rAyzmxUb2i+vcYcD29DejlwHn0NqDD2qSZ2nLT/pP+SdKMPQV8srr84+r/d0TENyLizupL8k7gi9V1DwA7RrRJap7fi6UWjDtVeNRG9a3AE8Bv0tuA3lldd2wDevGQttZHgy1O5tehJ5/hhur9XcSRKEndl5nfBYiItwFnAo8DH8nM/RHxMPBm4Hzgmeomz9Lb0zOs7RQRsRvYDbC0tMTKysopy6yurg5tb9KebS+e0rZ09vD2NpWQYZY5Bj8H/Tn6r1sr27Q/S7P4fK6j0e/FdfpsG9Z63Wfx2Sylbx4zrTyf2ve545e3bd50/PLgfa/1OSitj0wrz1iFa5Mb1Y10zsE38FP7PsfS2b3/92wb55mdrO4KeZzbrKysHH8T6953/wd53Oc3Kt/g6zx43ais43wI13rcOrfpfx161524PGqDutbjTuM5lSYizgD+KjOvjYizqDE9qW6b05ik5kXEW4APAtfS284erq46DPw0cBQ49o1mU/X3uUPaTpGZe+lNZWT79u25vLx8yjIrKysMa2/SDUMGlfdse5FbD832dBwlZJhljsPXLZ/096f2fe54jv7rhr1/o+5jUrP4fK6l6cGmOn22DWu97mu9/00ppW8e00SetfrYWv2qtD4yrTxjv7pNbVQ30jmb3sjVXSGf5NBzfX+MznH4uuXjb2KbnX3Ucxr88A9eNyrrOBujUY+71tz9uq9R//u/3nMadd/T3sC2LSLOBr4O/HzVdGwq0nrHwtVtm9osCY+jkk4VEa8CPgRcnZnPRcTNwHci4g7gUuAP6W1Pr6K3t2Yn8Al6/XWwTZq5RVjXNznYpMW11izRRehXg8Y9OZMbVc2trq8IMvN54LKI+F7VVHd6Ut22kwrXSWZJ9GtiT3dpU2U2qsv5zT6R9wCvBu7pHc7KF4D3Ah8A7srMb0fE48DbI+IgcAC4n96X5cE2SQ3ze7HUjnF3TbpR1VQ0ebyxxzIfV3d60ljTmCadJXFME3u6h01N6dJZ+Uqb6rMRZh9fZn4M+NhA880Dy7wA7BpYZlib1JhxtrNdWgdvgN+LpRaMe4yrG1WpO+pOT6rbJkmSKn4vltpRzhHNkppyP/WmJ9VtkyQtiME9pNM4+aUkjWPc33GV1B37gM3VVKSn6RWyk7RJkiRJrXKP6xo8RlJdlpmXVP/XnZ7kNCZJrXEbK+j+CREltcfCdUa23LSfPdtenMnvXnWFX2okSZKktZ06pf9EjTFPA0JOFZYkSZIkFc09rtIa6k5hcqqTJElaJM6M0yiDn43brz5nKvdr4SpJkiRJc2icAYZSd8I4VViSJEmSVDT3uGpdnkhKXTHq5ASljhxKkiR1xeD3rLa/X1m4ampGHefpMRDS7Ff2kiRJdZT63d3CdcG09UEs9QOv+VL652ycfP232bPtRZanmKfu41pUS5IGHXryGWffaaYsXDWR0guHUSbN3dXnrfEtcmFXdzbFor0ukprj+kXSIAtXScVb5KKxq/pH5n3PJNXhoLDULW1/P7NwlaQh6n6Bcq+AJEladG0MPFm4StIGlbBXYNH2Qo96zRfhuUuSJAtXqbYSipV51mQhVvp7N6uTpjX5Ou/ZNtntLUglSVI/C1dJnbLIRWjpz32UtXKPKlC7+lwlSVIzLFwF+CVRmifuuZQkSfOm1cI1Is4CPgtcBBwE3p2Z2WYGSfXNqs/OaiClzcd1sKinyZ+mWsSifdp91pOPSc1qss9Oo79OegiINE1t73G9HjiSmbsi4m7gSuDeljNIqm/u+2zpBWSdfGstM851pb8mdS3onue577NaTP39+farz5lhkqmzz0o1tV247gTurC4/AOzAzimVzD6rk3S1qF2gPbMz77Nd/YxIMzLzPjvIPqxSRZszdSPiHuCPMvO+iHg/8PrM/O2BZXYDu6s/twKPbfBhLgCOThy2HWadvi7kvDgzL5x1iDpa6rNN6sLnYS1dzj9P2Rexz5by/pWQo4QMYI6NZFjEPtuGEt77fuYZraQsMKU+2/Ye16PApuryJoY8gczcC+wd9wEi4pHM3D7u7dtk1unrSs4OabzPNqnrn4cu5zf7zEylz5byGpSQo4QM5igvwxR1Zjtb2utuntFKygLTy3PaNMJswP3AVdXlncCDLT++pI2xz0rdYp+VusU+K9XUduG6D9gcEQeBp+l1Vknlss9K3WKflbrFPivV1OpU4cx8AdjV8MPMfCrFBph1+rqSsxNa6rNN6vrnocv5zT4DU+yzpbwGJeQoIQOYo18JGaaiY9vZ0l5384xWUhaYUp5WT84kSZIkSdJGtT1VWJIkSZKkDelk4RoRZ0XE3RFxICLuiIgYskxExJ9HxNci4q8jou0zKNfO2rfs70fEfW3m63vsWjkj4g8i4ssR8YWIOLPtnFWGOu//ORHxuYj4SkT8q1nkVDu60seG6VK/GzQP/TAizoiIz69xfe3PVhfVfA8bfQ1K2Z6Xsh4pYZ1QWt9e9H7atlL65Eby9C3b6Da+hP650Txtb4eb7q+dLFyB64EjmXk5cB5w5ZBl3gicnplvAF7OiTO2ta1OViLiYuCGFnMNWjdnRPwc8NrMfBPwBeBn2o14XJ3X9Drga5n5RuC1EfGLbQZUq7rSx4bpUr8b1Ol+GBFnA48y4vNSqfXZ6rA6z6/p16CU7Xkp65ES1gnF9G376UyU0ic3kqetbXwJ/XNDeWhxO9xGf+1q4boT+GJ1+QFgx5BlngI+WV3+cRuhRqiTFXpZP9xKouHq5Pw14LyI+BLwJuDvW8o2qE7WF4CfqkZyzmK2nwE1qyt9bJgu9btBne6Hmfl8Zl4GHFljsbqfra6q8/yafg1K2Z6Xsh4pYZ1QTN+2n85EKX1yI3mgnW18Cf1zo3la2w630V+7WrieDzxTXX4WeOXgApn53cz8RkS8DTgTuKfFfP3WzRoR7wIOAN9uMdegdXMCFwI/zMxfpTeCdEVL2QbVyfrvgN8A/jPwd5n5eEvZ1L6u9LFhutTvBi1CP6zzHLuszvNr+jUoZXteynqkhHVC1/r2vPfTtpXSJ2vnaXEbX0L/3GiekvoqTNhfu1q4HgU2VZc3VX+fIiLeAnwQuDYzX2op26A6WXfRG6H5DPC6iPhAS9n61cn5LPBYdfm/AJtbyDVMnawfBv4kM38BeGVE/Epb4dS6rvSxYbrU7wYtQj+sta3psDrPr+nXoJTteSnrkRLWCV3r2/PeT9tWSp/cSJ62tvEl9M+N5impr8KE/bWrhev9nJhPvxN4cHCBiHgV8CHgmsz8UYvZBq2bNTPflZlXAO8EHs3M21rMd8y6OenNW399dfkSeh1yFupk/SfAP1aXXwDObSGXZqMrfWyYLvW7QYvQD+s8xy6r8/yafg1K2Z6Xsh4pYZ3Qtb497/20baX0ydp5WtzGl9A/N5qnpL4KE/bXrhau+4DNEXEQeBp4PCI+PrDMe4BXA/dExEMR8b62Q1bqZC3Bujkz86vA0Yj4W+CxzPzGDHJCvdf0j4F/ERFfBc6m11E0n7rSx4bpUr8bNFf9MCJ+dkj+wedYbP4x1XkPm34NStmel7IeKWGdUGzfXtB+2rZS+uRG8rSlhP65oTzMcDvcRH+NzJxWPkmSJEmSpq6re1wlSZIkSQvCwlWSJEmSVDQLV0mSJElS0SxcJUmSJElFs3CVJEmSJBXNwlWSJEmSVDQLV0mSJElS0SxcJUmSJElFs3CVJEmSJBXNwlWSJEmSVDQLV21IRPxyRByKiP87In5v1nkkjRYR/11E/F1E/DAibpx1HkmSpHFZuGqkiDgcEVsGmv8tcCOwFfjdiPjFtnNJOtWI/vqnwEeBS4EPRcRr284lSZI0DRauqi0iAtgGfCkzfwQcrP6WVJiI2AT8t8D+zHwKuAe4ZrapJEmSxmPhOoci4rqI+PuIeCoiPlS1XV/tkfl+RNxQtS1HxEpE3BoR/29EfCkizo6IfxkRR4GLgP8YEUcj4pzMTOAA8GsR8UrgMnrFq6QxNdVfgTOqh3ip+v854OKWn54kSdJUWLjOmWrq7v8O/Cq96YE3RsRlwMeANwNXAP9rRBzbU/oG4AngVcArgN/IzH+dmRdU7b+cmRdk5nPV8n8A/DXw/wAPZObftfTUpLnTMpuOJAAAEXlJREFUZH/NzKPA/wVcExEXAr/OiWJWkiSpUyxc58+v05sa+ERm/hD4GXpfgD+fmd/PzCeAvwKuqpb/IfDJzHwB+E/Ay0fdcUS8nN4xrpcDm4CLIuJdzT0Vae411l8r7wduAT4HPFzdXpIkqXMsXOdT9F3+NU59n7Nvmb+vpgAfa1/LLwBPZ+a3MvN5esfMXTFpWGnBNdVfAX4AbM3MX6FX5DpDQpIkdZKF6/x5gN7UwIsi4hXAHwOPALsi4jURsRl4O72iE+Ana9zXUeBnI+K0iDgfeBzYEhGvjYiXAf898K3Gnok0/5rsrwD/HliOiEuAN9Lb8ypJktQ5Fq5zJjO/BfxPwJeBbwL/JjO/AtwEfInedMGPZuahGnf3UeB24B+AqzLzH4AP0Psy/ATwFL2f25A0hib7a9W2u2pbAX4nM5+dYnxJkqTWxIlZZ5IkSZIklcc9rpIkSZKkolm4SpIkSZKKZuEqSZIkSSqahaskSZIkqWgWrpIkSZKkop0+6wBrecUrXpGXXHLJrGNsyHPPPcc555wz6xi1dS0vdD/zo48+ejQzL5xxpEZccMEFuWXLlpHXl/LelZDDDOVkWC/HPPdZSZK6oujCdWlpiUceeWTWMTZkZWWF5eXlWceorWt5ofuZI+L7s03TnC1btqzZZ0t570rIYYZyMqyXY577rCRJXeFUYUmSJElS0SxcJUmSJElFs3CVJEmSJBXNwlWSJEmSVLSiT840iS037T9++fAt18wwiSSdvE4CuP3q2Z9JV5IkqSvc4ypJkiRJKpqFqyRJkiSpaBaukiRJkqSiWbhKkiRJkopm4SpJkiRJKpqFqyRJkiSpaBaukiRJkqSiWbhKkiRJkop2+qwDSFKXbblp/0l/H77lmhklkSRJml/ucZUkSZIkFc3CVZIkSZJUNAtXSZqBQ08+w5ab9p8y1ViSJEmnqlW4RsQZEfH56vLVEXEkIh6q/m2NiLMi4u6IOBARd0RPrbZmn54kSZIkqevWLVwj4mzgUeDKvuZPZ+YV1b/HgOuBI5l5OXBetWzdNklT5mCTJEmS5sm6hWtmPp+ZlwFH+prfERHfiIg7qy+yO4EvVtc9AOzYQJukKXKwSZIkSfNmnJ/DeRz4SGbuj4iHgTcD5wPPVNc/C2zdQNtJImI3sBvgwgsvZGVlZYyIsGfbi8cvj3sf41hdXW318SbVtbxg5vVk5vPAZRHxvb7md0TEW4EngN+kN4h0Z3XdsUGki2u23dvoE5AkSZIGjFO4Pg3cV10+DPw0cBTYVLVtqv4+t2bbSTJzL7AXYOvWrbm8vDxGRLih74Qnh68b7z7GsbKywriZZ6FrecHMY2htsGlpaWnNAr2UQYdp5ugfJIPRA2WDyy2dfaJtVq9JCe9HCRlKyiFJkoYbp3C9EfhORNwBXAr8Ib2C9Cp6e2Z2Ap8AXlOzTVKzWhts2r59+5qDTaUMOkwzxw0DZwUeNVA2uNyebS9y66HT17xN00p4P0rIUFIOSZI03Dg/h3Mb8F7g68BdmfltYB+wOSIO0vuSfP8G2iQ160bgnRFxGr3Bpm/S63tXVdfvBB7cQJskSZLUqtp7XDPzkur/HwDLA9e9AOwauEndNknNug34C+ADVINNEfE48PZqEOkAvQL1zJptkiRJUqvGmSosqQMcbJIkSdK8GGeqsCRJkiRJrbFwlSRJkiQVzcJVkiRJklQ0C1dJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklQ0C1dJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklQ0C1dJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklQ0C1dJkiRJUtEsXCVJkiRJRbNwlSRJkiQVzcJVkiRJklS002cdQJJ0wpab9p/09+FbrplREkmSpHJ0unD1C54kSZIkzT+nCkuSJEmSimbhKkmSJEkqmoWrJEmSJKloFq6SJEmSpKJZuEqSJEmSitbpswq3qf8Mxp69WJIkSZLa4x5XSZIkSVLRahWuEXFGRHy+unxWRNwdEQci4o7oGbut2acnSZIkSeq6dQvXiDgbeBS4smq6HjiSmZcD51Xtk7RJmjIHmyRJkjRP1i1cM/P5zLwMOFI17QS+WF1+ANgxYZukKXKwSZIkSfNmnJMznQ88U11+Ftg6YdtJImI3sBvgwgsvZGVlZWSQPdtePOnv/mX7r1vrPuqqe3+rq6tTeby2dC0vmHk9mfk8cFlEfK9q2gncWV0+NmB08QRt9/Y/Xn+fXVpa6kT/mGaOtdZDay23dPaJtlHrrrXubxpKeD9KyFBSDkmSNNw4hetRYFN1eVP197kTtJ0kM/cCewG2bt2ay8vLI4Pc0HemX4DD1y0Pva6/fVx1729lZYW1Mpema3nBzGNodLCpv89u3759zT5byns3zRxrrYfWWm7Pthe59dDpp9ym7v1NQwnvRwkZSsohSZKGG+eswvcDV1WXdwIPTtgmqVnDBpsmaVOLtty0//g/SZKkRTVO4boP2BwRB4Gn6RWjk7RJapaDTZIkSeq02lOFM/OS6v8XgF0DV0/SJqlZ+4C3VwNGB+gVo2dO0CZJkiS1apxjXCV1gINNkiRJmhcWrpLUQf3HvB6+5ZoZJpEkSWreOMe4SpIkSZLUGve4StIGeYZfSZKkdrnHVZIkSZJUNAtXSZIkSVLRLFwlSZIkSUWzcJUkSZIkFc3CVZIkSZJUNM8qLEkz5lmKJUmS1uYeV0mSJElS0dzjKkkNcU+qJEnSdCx84dr/xfLwLdfMMIkkSZIkaZiFL1wlSScb3FPsoJ4kSZo1C9c1OM1PkiRJkmZvIQtXC1JJkiRJ6o6FLFwlqSmzGBhzaq8kSZp3/hyOJEmSJKloFq6SJEmSpKI5VbiPx75KkiRJUnnc4ypJkiRJKpp7XBdM/15lT+AiSZIkqQssXCVJc8PBOUmS5pNThSVJkiRJRXOP65xwL4MkSZKkeeUeV0mSJElS0SxcJUmSJElFs3CVJEmSJBXNwlWSJEmSVLSxTs4UEVcDfwocrpp+B7gZuAg4CLwbeBnw2fXaMjPHjy9pPfbX+dF/EjZJkqRFMske109n5hWZeQXweuBIZl4OnAdcCVxfs21ubblp/0n/pBmyv0qSJKmzJvk5nHdExFuBJ4Af09szA/AAsAO4GLizRtu9E2SQVI/9VZIkSZ0V48z8i4j/Bvj5zNwfEQ8DrwOuycz7IuL99PbobAH+aL22zPztgfveDewGuPDCC1/3l3/5lyNzHHrymVp5t23eNNbt6t5fv9XVVc4999yhj7PW7SbV/1hrPc7gcv15u6LrmXfs2PFoZm5v67Gb7K/V/R/vs0tLS6/7zGc+MzJLKe/dpDkmXYcALJ0NTz0/8d0MVXddM+p1aHPdNe3PRN114UZytN1nJUnSqcbd4/o0cF91+TDwS8CxbwibgKPAuTXbTpKZe4G9AFu3bs3l5eWRIW6oOf328HUn30fd29W9v34rKyscyzz4OGvdblL9j7XW4wwu15+3K8y8YY31Vzi5z27fvn3NPlvKezdpjknXIQB7tr3IrYcmmfQyWt11zajXoc1117Q/E3XXhU3nkCRJ0zXuMa43Au+MiNOAS4E9wFXVdTuBB4H7a7ZJapb9VZIkSZ02buF6G/Be4OvAXcCfAZsj4iC9vTv3A/tqtklqlv1VkiRJnTbWPLXM/AGwPNC8a+DvF2q2SWqQ/VWSJEld18wBVnNu8KdtDt9yzUT30X/7ady3JEmSJM0TC9cp6C82b7/6nBkmacaoInvc5STNxuDA2DyuryRJ0nwa9xhXSZIkSZJa4R7XwrkXU5IkSdKis3BVIyy4JUmSJE3LQhSug8d1zTuLRkmSJEnzZCEKV0ma1LwPgM3785MkSd1m4SpJWpOzOCRJ0qxZuHbItL88brlpP3u2vcgNN+33y6g0Ryw0JUnSvLFw7ai60/rGXc4vu5IkSZJKYeGqobp0vJt7lzQtfpYkSZLKZOEqSSqCAweSJGmU02YdQJIkSZKktbjHVROZ5pRij7OVylfaXtEuHdYgSZLGZ+HaIr9gSZIkSdLGWbiqcfOyJ7W0PU2aDgeUJEmSymfhqk7oLy5uv/qcGSaRuqWEwnxeBq8kSdLsWLhqIbn3VOspoeCTJElSj4XrlB168hlu8Atvo3yNJUmSpMVi4aoNmcZeqNL2do4zjdGpj5IkSVJ7LFwL4JTE7iutGJdkv5QkaZ6cNusAkiRJkiStxcJVkiRJklQ0pwprpkqfJl16PknjcRqxJEndYuEqSZqKJovBSe/bQShJkrrNwlWSNDP+vJUkSarDwlUa4J4ZqZ5x+0r/7fZsm+59S5Kk+WThqmKN88V1remEfhFeXIPv/Z5tL7qXD/d2SpKk7vCswpIkSZKkorW6xzUizgI+C1wEHATenZnZZgZJ9XW5z7qHXZIkaX60PVX4euBIZu6KiLuBK4F7W84gqT77rMbiwIEkSZqmtgvXncCd1eUHgB34JVgq2VT77OAxlZP+ZIrF0WLyfZckafFEm7P+IuIe4I8y876IeD/w+sz87YFldgO7qz8vBb7ZWsDpuAA4OusQG9C1vND9zBdn5oWzDFPXGH12K/DYGndZyntXQg4zlJMB1s7RmT4rSdK8anuP61FgU3V5E0O+JGTmXmAvQEQ8kpnb24s3ua5l7lpeMHPLNtRn11PK61BCDjOUk6GkHJIkabi2zyp8P3BVdXkn8GDLjy9pY+yzkiRJmrm2C9d9wOaIOAg8Te9LsaRy2WclSZI0c61OFc7MF4BdG7hJremHhela5q7lBTO3Zow+u55SXocScpihp4QMUE4OSZI0RKsnZ5IkSZIkaaPaniosSZIkSdKGWLhKkiRJkoo288I1Is6KiLsj4kBE3BERMWSZiIg/j4ivRcRfR0TbP+MzmGfdzH3L/n5E3NdmviEZauWNiD+IiC9HxBci4sy2cw5kqfO5OCciPhcRX4mIfzWLnIMi4oyI+Pwa19f+7HRVzfeu0dehlPVKCeuKEvp/Sf3ZPipJUjfNvHAFrgeOZOblwHnAlUOWeSNwema+AXg5J36eY1bqZCYiLgZuaDHXKOvmjYifA16bmW8CvgD8TLsRT1HnNb4O+FpmvhF4bUT8YpsBB0XE2cCjjPg8VGp9djquznNs+nUoZb1SwrqihP5fRH+2j0qS1F0lFK47gS9Wlx8AdgxZ5ingk9XlH7cRah11MkMv84dbSbS2Onl/DTgvIr4EvAn4+5ayjVIn8wvAT1V7RM5ixp+NzHw+My8DjqyxWN3PTpfVeY5Nvw6lrFdKWFeU0P+L6M/2UUmSuquEwvV84Jnq8rPAKwcXyMzvZuY3IuJtwJnAPS3mG2bdzBHxLuAA8O0Wc42ybl7gQuCHmfmr9Pa2XNFStlHqZP53wG8A/xn4u8x8vKVsk6jzvLquznNs+nUoZb1SwrqihP7fpf68CH1UkqTOKaFwPQpsqi5vqv4+RUS8BfggcG1mvtRStlHqZN5Fby/GZ4DXRcQHWso2TJ28zwKPVZf/C7C5hVxrqZP5w8CfZOYvAK+MiF9pK9wEan3eO67Oc2z6dShlvVLCuqKE/t+l/rwIfVSSpM4poXC9nxPHlu0EHhxcICJeBXwIuCYzf9RitlHWzZyZ78rMK4B3Ao9m5m0t5hu0bl56x329vrp8Cb0vr7NUJ/M/Af6xuvwCcG4LuSZV53l1XZ3n2PTrUMp6pYR1RQn9v0v9eRH6qCRJnVNC4boP2BwRB4Gngccj4uMDy7wHeDVwT0Q8FBHvazvkgDqZS7Ju3sz8KnA0Iv4WeCwzvzGDnP3qvMZ/DPyLiPgqcDa9L5zFiIifHZJ58HkVlXlK6rx3Tb8OpaxXSlhXlND/i+zPC9xHJUnqnMjMWWeQJEmSJGmkEva4SpIkSZI0koWrJEmSJKloFq6SJEmSpKJZuEqSJEmSimbhKkmSJEkqmoWrJEmSJKlo/z88LDs23nCQDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.7 s\n"
     ]
    }
   ],
   "source": [
    "# One thing we can do is to plot histogram of the numerical features and analyze their distributions:\n",
    "train[cont_features].hist(bins=50,figsize=(16,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a86f42610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0wAAAIKCAYAAAAQ689yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd4FFXbwOHfZLPZTTY9pJACCFJDkyoJVZqABQRsFEEERKWprwVEpIgNRFEBwYKg2AUsr4VXihBQQJASQodAIIWQ3svO98cuAZJNsn5mC/G5r2svssMzyXN2zpQz55xZRVVVhBBCCCGEEEJU5OLoBIQQQgghhBDCWUmDSQghhBBCCCEqIQ0mIYQQQgghhKiENJiEEEIIIYQQohLSYBJCCCGEEEKISkiDSQghhBBCCCEqIQ0mIYQQQgghhNNTFEWrKMp3Vfy/XlGU7xVF2a8oyhpFUZSa+LvSYBJCCCGEEEI4NUVR3IE/gb5VhI0EElRVbQP4VRNrNWkwCSGEEEIIIZyaqqr5qqq2BhKqCLsF2Gj+eRPQqyb+tjSYhBBCCCGEELVBAJBp/jkL8K+JX+paE7/EkuLUU6qtfre9TejwH0enUGPWXPjd0SnUmOPNWzg6hRrzVmaAo1OoMS8ubOnoFGpM0rytjk6hxqjGGhnG7RTCvn7e0SnUGOOZA45OocYYt252dAo1R2uzyyO7K01Mc3QKNWb5T4GOTqHGPBX/8XV3ULbHtb1bYKOJwISrFq1QVXXF3/gVqYCP+Wcf8/t/rPYcEYQQQgghhBDXLXPj6O80kMr7FegHfI1peN7imshLhuQJIYQQQgghqmYstf3rb1AU5QZFURaWW/wJEKYoygEgDVMD6h+THiYhhBBCCCHEdUFV1RvN/54Gniz3f4XAbTX9N6XBJIQQQgghhKiaanR0Bg4jQ/KEEEIIIYQQohLSwySEEEIIIYSomlF6mIQQQgghhBBClCM9TEIIIYQQQogqqTKHSQghhBBCCCFEedLDJIQQQgghhKiazGESQgghhBBCCFGe9DAJIYQQQgghqvYvnsN0XTeYiktKmDZjHu+8OsfRqVTKVafl0aVP4h9ah4S4eFY+vsTqODd3HQ8vmYannzfH9xzhy5fX0HNEP24dfweZFzMAeOPBBeRn59mzSADodDq++GwF4RGhHDwYx5ixU6qMnzplPAMH9Kb/gHuZMH4Uj0+fSHLyRQBuv3M0WVnZ9kj7GoqblqBFz6MJDqT4+GkuznylYpDGhaCXZ6IJDKA4/hypsxeh79Aav8fGAuBaN4j0t1eR891GO2d/hatOywNLp+MXGsCFuLOsffydvxV336JJBDUMJTs1k1UPv06Trq24++UJpCWYts/nT7/LxVOJditPYXEpT366leTMPBqH+DJ/WDSKolwTs/tUEu/87y8AEjNyebRPW+5o14gPf4vltyMJeLi58sbInmhdNXbL25LaUsfAXJbXZ+EaEkjRsdNcnPFqxSCNC0GvzsA1MIDiM+e4+PzrAPiMHY5Hj5tR8wtImjwbSkrsnP21CouKeeKlpSSlptGkQTgvPv5QhTqWlZPLtBffoaS0lOh2LZl47+2UlJby9GvvcjEtkwZhwcyd+qCDSnBFYXEJT77/A8npOTQOq8P8Uf0qlCW/sJhnP/qJ9Jx82jYMZfrgrmXLZ338CwvHDXJE6lVz1aK77wkUnwDUpLMUfvWWxTBttzvQNOuAWlRA4cevQqlj61YZVy26u6eheAegJp+lcN1Si2Ha6NvRNGlnyv+zhVBaCloduiGTKPziDTsnXQlXLe4TZ+HiF0jp+VMUfPhahRBNZAfcR03DeCkZgILVizFevID7Q8+i+ARgTE6gYPXr9s78Sn46LYOXTcGrrj8Xj5zjh+nLrYq5oUdrbn3lITLN58OfnnoPQ5Av3Z4cBoB3WB22LfyK2K+32bU8DmMsdXQGDnPdDskrKCzk7gcns3P3PkenUqWowd1JT7rE7AFP4OFjILJbG6vjugzuzsl9x1gwbCZhTSKo2ygMgPWLP+el4c/x0vDnHNJYAhhx/10knE+kfYe++Pn60LdP90pj69ULY/Tou69ZNmfeInr0GkKPXkMc0lgCMAzqQ0lyKhfufhgXb0/cu7SvEOPRK5qiYydJHDMNTR1/3Jo2omDPARLHTCdxzHSKjp2m8MgJB2R/RfvBXclMusTCAU/j7mOgSbfWVsfd0KEpLhoNbw6Zhd7Lg6bmdXd8vJG3h7/A28NfsGtjCeCHv04R7O3BF5NvIyu/iJ0nKv79jg1DWDXhVlZNuJXGwX40C/UnIS2bkykZfDihP9FNQknOcsy+cbXaUscAPG/rTWlyKueHTaq0LIZboik6eooLo6ejCQzArWlDXMNDcGvUgMQxT5C3bTeuIXUckP21vt+8k+A6fnz11hyycvLYuS+2Qsx/t/5Bo3qhrH71WfbFnSAh6SKbft9H0xsiWP3qs1xMy+TIqbMOyP5aP+w+QrCvJ188O4KsvAJ2HqmY0w97jtCqQQgfPX43JxMvcSopjfOpmYxY+BlHzReCzsa1bTfUrEsUvP0fcDegubHicU3xC0IJiqBg5fOUHtuH4u3vgEwtc23dFTUrjYLlz5jyb9SqQoziF4QSGE7Bh3MoPfEXincAim8g7uPn4xJS3wFZW6bt3Bs1PZXc+ZNQPLzQNG9nMa5o6w/kvfYEea89gTE5Ade2UZQmnCLvtcdx8fHHJbyhnTO/InJINNmJaawaMBO9j4EG3VpaHbPv4/+xdtg81g6bR9qpRM79Hlf2/uKRc6TEnrFzaYQjXLcNJr1Ox7rVywgOdPzJtyrNo1oRu+0AAHE7DtG8S8WdtLK4kqJi3PQ6ALQ6LSXFpjtnvR8YwAs/vMb9sx13d7NXr2j+9+tvAGzeEkPPnlGVxi5+fS7PPffSNcsenTSW3bt+5vVFjusddO/UlvydfwKQv+sv9B0rNmbzY3aTueZr0Ljg4uWJMSe37P8UvQ7XeqEUHz9tt5wtaRzVkmPbDgJwYschGndpYXVcdmom2z78EYDSoit3ZlsP6My09fMZs2y6jbOvaNepJG6+sS4AnRqGsOdUUqWx+UUlnEvLpkmIH3+cTCI7v4gHV/7M3vgUwvw87ZVypWpLHQNw79yW/J17ASjYtR99p4plydu+m8zVl8tiwJibh3vnm3Dx9qTuqkXo27ekJKHy7Wkvuw4c4ea2pv2kU5tm7D541GJcbn4BqqqCqnL09Dm6tmvJqDv7UVJaSnZuHp4e7vZM26Jdx85xc7N6AHRqEsGe4wkVYtxcNRQUlaCqKkUlJWg1LoTV8eGbmaPsna7VNA1bUXrCdE4sPXUIl4YVz52aRq1Q3D3RPzQHTf3mqOkp9k6zUpobIik9aTrelp6OxaVBpIWYlijuBvRjn0dTrxlqegpqxkXyl/7H3ulWybVZW0riTPt+6dG/cG1q+cavtl00hmeW4D5xFgAlsXso2vgNuLiAhwG1wHE3sepFteDM9kMAxO84TD0L58nKYpoO6MSoDXO4c/m1I2lc9W741Q/m4pFzNs7eiahG27+c1HXbYHJWo+aN59kv55e9fIL8ynqBCnLyMPhavojz9POqEPf7hm206nkTC35dQuLJ81w8m0z8wZN8/uJHzLn9adr160RAeKBdyvXWkgVs3byu7FU3JJisTFPPUFZWNn5+fhbXu/fewRw4cJjDccfKlu3de4Cnnp5L55sHMPjOAdSvH26XMpTn4utddnFqzMnDxcerQoyaX4BaUEjdj96g9FI6JeevXOy539yOgj/s38M5dN6DPPblC2Uv7yDfq+pOPh6V1DEPP88Kcalnkji7/ySt+ndE4+bKkd/2kxqfzI+LvuCNwc/hFeRHo5stN8BsJTOvEE+9GwAGnZbM/KJKY38/kUinhiEApOcW4GfQ8cH4/qRk5rEv3vEXT9drHbPExefqsuSiqaIsoavfoPRSBiUJSWj8fChNzyRxzBO4BtdB387yTSN7yszOwcvgAYDB3Z3M7NwKMQN73Ex2bh6Pv7QUrdaVgsIiPNz1uOt1jH7qJQJ8vQkPsc/xtyqZuQV4mm+sGfRuZOYVVIgZ0KEpMYfPMGT+GhoE+xMR6GvvNP8+D88rF9iFeSjuFY9risEbNTeLgvdmo/gE4FK/mZ2TrIK7J2rh5fzzUdwNFUIUg5cp/w/nonj741KvqZ2TtI5i8ELNN+0jan4eisHCvn/xAoUbVpP78hQUH380TVpDYQEUF2J4ajFqVgZqquNulrj7elJoHnVQlJOP3sJ50lJMRnwy2xZ9xZo7Z+MZ5EvEzc3L4ht0a0n8joq906J2qrLBpChKaGWvSuInKIqyR1GUPe+t/tQ2GTu5NbNWlg2Xe2n4c2Qkp+HuZToxu3t5kJ1mefhZdlpWhbhBj9zFlk9+YUbvKRh8vbixXVMuXUjl5L7jqEYj6UmX8A7wsUu5Jk+ZUTaErkevIVxITMLbfMHk4+PNpUtpFtcbNLAPt/TqytqPl9GuXSsemTSGs+fO8/sfezEajZw/n0iQg3oJjemZuHiaTmIuXgaM6VkVYlx8vECrJXH0NFy8va7pIfDo0YW83/6wW76XfT3rg7Lhcm8Pf4HM5PSyuqP38iCnkjqWm5ZtMS6yT3u6jR3A++NeRTWq5GXkcCzGdGc0PeEingHedijVFb4GHTkFpkZSTmExvh66SmN/O5JA92amBrenTkv9Oqb9IczfkxQnGJJ3vdYxS0ozsq6UxdNAaXpmhZjLZbkwahou3p7oO7bBmJtH8RnTHdjihCQ0QY4fFeDr7Ul2rql+5OTl4+tt+SbDnCljWTzjUdy0rgT4epORlUNRcTFrXp1BVk4euw4csWfaFvl6upNTUAhATn4RvoaKvV4f/LKHYV1bsX7WaLJyC/jr1AV7p/n35Waj6E3HK3QeqHkVj2tqYT5q6nnTz2nJTjUkj7xsFJ0V+V8yDTlW01OcK/+rqDlZZQ0+xd2AmlPxOKbmZlNyxHRzR72UjOLla2pYuWrJfWU6iocnmiaWe6bsIT89G523aXvovNzJT6+4PSzF5GfkEm/udcpMSMXjqvPhjX3acfJX57ihZTdGo+1fTqq6HqbZwAngY+CTq14fWwpWVXWFqqodVFXt8NDo+2o00etVXMxBWnY3HSSaR7XiyM5DVsfpDe4UFxYDUFJUjM6g597nxtCkYzO0Ojf8QwNJPm3f+SWXbdq0nb59egDQq2c0W7bssBg3avRj9Og1hPtHTmLv3oMsXbaKha/Npmt0J/R6PRERYRw/4ZjhRvm79uEeZZqH4d6xLfm7/6oQ4zN6OIZ+3cFoRC0oQNFduXjXd2xDwS7HHyyPxxyiaXfT+P7GUZGc2Gn5jpelOK9AH3pNuJ33xr5CYa7pznSPhwZx0+1RKIpCSJNwko7ad7hB54Z1y+Yt7TqZREdzD1J5qqqy+3RSWQ9T87AAYs+nAnDuUjZhfhXvgtpbbaljAAW/X1WWzm0p2L2/QozPA8Pw7H+5LIUoejcKDx9H19J051wbEUpJgmOOWVfr3KY5O/cdBmDXgTg6ta7YM/Fn7DHmvbOaouJijp1OoHXThny0/md+2b4HjcYFvc6NwqLKez/tpXOTiLJ5S7uOnaNj44o99rmFRei0pmc8aV015JnPK86s9NRBNDeazomaRq0oPVXx3Gk8fwqXsBsBUAJCnGpIXunp2LJ5S5obIik9c7hCjPHCaVxCTfN6FH/nyv9qJUf24drCNG9J06wNJUcr7vtufYai7dADFAWX0PoYL5zBre8wtO27mYZZFRWiuLnZO/Uy8TGxNOhm2h71oiI5u6Pi9rAU03H8AJrd3gUUhTpNwkk9duV8GNGlOfEWfo+onaprMD0M/K6q6i2qqva66nWLPZKrDXZu+A3fYH/m/vg6uRk5HI45QJ3wIO6ZMbrauF/X/Eivkf2Z+c0C3PRuHI45yPfvfMOwp0cy46v5fLvkS/KyKg4lsYe1n64jLDSEvX9uJC09g183baNBgwhefXlWteu+/MpbLHhxBlu3rGP+i4vJyKh4p9oecn7YhGtQHcK+fBdjVjYlCYn4Pz7hmpiszzfgNbg/dVe/iTEzi/wdewBwa9mUopPxqEWOv/D4c8N2vIP9efLHV8jLyOV4zCH8wwO5fcbIauM6Du2Bd5AvE1Y/y2NfvkCn4T3Z/tHPdBrek6nr53Pwl90knzhv1/IMbHsDKVl5DF/yHT4ebkT4e/L6j39WiDuUcIlGQb7otKYn4bWpF4ifh577l/6XBnV8aBXh+J6M2lLHALJ/2IQmKICwr5dTmplN8blE/J8Yf01M1mff4jW4P6Efv0FpRhb5MX9SuD8OY3omoZ++RfGZcxQesjxfyJ4G9byZlEvpDJ08Gx9PA+EhgSx8//NrYrq2b0lRcTFjnn6F8ffchoe7nnsH3cK6/21n5JMv4uvtSdRNjh9eOLBDU1Iychj+0sf4GPRE1PHh9XXXPrHrnm5t+GLbAUYv+pzC4hI6N41wULbWK/lrG4q3P+6TF0JeDmpaMm63XjvnynjuGGpeNvpJL2FMvYAxwfEPR7ms5MB2U/6TXoF8c/79RlwTY0w4bsp//HyMly5gPH/SQdlWrXjXZhTfOhhmLUPNzcZ48QK6odfu+0Wbv0Ub1Q/DM29S8tcOjIlnKdryLdqo/ng8vRhjbhYlsRWP4/ZyeP0OvEL8GPPTAgoycsg4m0LPmfdVGRMfE8vejzbSanh3Rm2Yw/Gf93DpuKl3NqRNQy4dO0/pdXDzoSapqtHmL2elqKpqk19cnHrKNr/YASZ0cK4JmP/Emgu/OzqFGnO8uX3n1tjSW5kBjk6hxry40PEXkTUlad5WR6dQY1SjUn3QdSLs6+cdnUKNMZ454OgUaoxx62ZHp1BztNf1t65cozTR8pD569Hynxw/b7CmPBX/8XV3UC48+bvNr+11jW52ys/Fqoc+KIoSUO79cNukI4QQQgghhHA6MoepWuvKva/6W0qFEEIIIYQQohaoss9ZUZQeQE+gnqIol8dAeAK1p39XCCGEEEIIUTUnnmNka9UN0j0DbAGGApcH8+cDzvHYJiGEEEIIIYSwoSobTKqqxgPxiqJ8oKpq7Zn9LIQQQgghhLCesdTRGTiMtY+B+VlRlGeAsofoq6o61zYpCSGEEEIIIYRzsLbB9BXwAuCc36omhBBCCCGEsB2Zw1Sti8A6VVVLbJmMEEIIIYQQQjgTaxtMe4AtiqKsBXIAVFVdbbOshBBCCCGEEM7Dib8nydasbTAdNL9UwCm/gVcIIYQQQgghapq1X1y7FtAB7QAt8InNMhJCCCGEEEI4F9Vo+5eTsrbB9CFQF/gRCANW2SohIYQQQgghhHAW1g7Ja6Cq6kjzzz8rirLdVgkJIYQQQgghnMy/eA6Toqpq9UGmhz3EAjuBLkCkqqr3V7XO2AZDq//F14kVe15zdAo1JuuBsY5OocYsPxTu6BRqTOvC2nMQ2q23tuPa+XmotWfKZnixozOoOX+61Z4HtoYbrb1v6fx8a89hjNpTwyBZU2sux0hRas+WefvM59fdCaZg/39tXpn0bQY65edi7ZXNw4AGGAZkARNtlpEQQgghhBBCOAlrG0yrgQTgUcAH05wmIYQQQgghxL+BPPShWkGqqn6gmswHgm2ZlBBCCCGEEEI4A2sHT8crivI0sAvoDFywXUpCCCGEEEIIp/IvfuiDtT1MY4A8THOYcoHRtkpICCGEEEIIIZyFVT1MqqoWAm/ZOBchhBBCCCGEM3LiOUa2Vnue/yuEEEIIIYQQNaz2fAGEEEIIIYQQwjaMpY7OwGGkh0kIIYQQQgghKiE9TEIIIYQQQoiqyRwmIYQQQgghhBDlSQ+TEEIIIYQQomryPUxCCCGEEEIIIcpzyh4mV52WR5c+iX9oHRLi4ln5+BKr49zcdTy8ZBqeft4c33OEL19eQ88R/bh1/B1kXswA4I0HF5CfnWfPIlWruKSEaTPm8c6rcxydStW0bnjNmINLnSBKz5wiZ9GLlYbqBw9H2/Fmsmc+AYDHuEdwbd6S0vNnyV38sr0yrkCj0zJ02VS86/qTcuQc305fZlWMonFhyFuP4RnsR9qpC3z/n5XovT0YtmI6LlpXTm3dz/Yl6x1QInDRaenw3jT0oQFkHz7LvslLLcYprho6fDCd3aMXAqD1MdDhw8dxcdWQsnk/xxevs2fa13DVabln6VR8Qv1JijvHN49X3C6Vxbi4arhv+TQ+eWhRWWzXibfRtPdNFOUVsnb8IkqL7fd0H41Oy5BlU8rqz/fTl/+tmI7jbqXRLW35bMTLGIJ8ue31ibgZ3Dm8YQd/rvrFbuW4motOS/cVUzCE+pMed44dUyqWCUx1rMf7U9nywOvXLG82/lbC+rTl13sct++DqQ6NWTod39AAEuPO8snj71gd56JxYdSSKfgE+5Fy6gKfPfWuxWX2oNFpuX35FLzq+pN65Bw/TrNcxyzFdHx4EA37tKM4t4D1D72O0bxv9HphFFp3Hb88/Z5dymAp31vevVLHtk6tvI71WTmVjWOv1LHuiyfi06gu+alZ/Dr+DdRSx94J1+i09H13Cp6h/lyKO8fmSsri4qqh38qp/HRVWSwtcwSNTsvwq86D6ys5V1qKuXPRRAIahpJ7KZMvJr4BwNC3HsMr2I/UUxf47j8r7VoWMO3TDy2djm9oHS7ExbO6in2/srhe4wYS2asdb4+cD8CQmaNo2L4Jyacu8PGTFT+fWkfmMDmXqMHdSU+6xOwBT+DhYyCyWxur47oM7s7JfcdYMGwmYU0iqNsoDID1iz/npeHP8dLw55yusVRQWMjdD05m5+59jk6lWrpefTGmXiRz8jgUT0+0N3WwGOcSGIyu961l711b34RaWEDWk49gTE5CMXjaK+UKWg2JJjsxjfcGzEDvY+CGbq2simnavwPJcWdZPXQOnkF+BLeoT+TgaC4eP8/qoXMIb98En4hAB5QIwod2Jf9CGr/1fgatr4HAHhXL5KLX0v2XBQR2v/J/YXdFkX00gZg7XsC/Y1Pc6zkmf4DWg6PJSkpj6YAZuPsYaGRhu1iKcdVpefi7+TTs1rIszi8ikMAmYbx/9zyObdmPd4i/PYtCpLn+fDBgJnofAw2uyq26GO+wAFoN61YW1/6Bfhz44jfWDHmBNvf0QOuhs1s5rtZwaDR5iWn80Hcmbj4G6vaoWCaNXsvAn+ZRt1x5DWEBNLq7W4V4R2g/uCuZSZdYOOBp3H0MNO3W2uq4Vv06ciEuniXDZuMd5Edoi/oWl9lDiyHR5CSmsebWmeh8DNS3UMcsxfjUCySgSTifD5vH6S378apr2jdC2jSkQU/Ln4W9NLormtzENNb3M9WxsO6W69jgH+cRelV5gzs2QXF14bs7XsDNy50wC8c/e2tsLstX/UyffXglZbnrx3mEXVUWS8scpfWQaLIS01hhPg82tHRMthAT0aEJLq4aPhgyG52nO426t6KZ+fz54dA5eJnPn/bWcXA3MpLSeHnAU3j4GGhWyb5fWZxfWB06D+tZFte4SyRFeQUsumsWaQkXcff2sEcxhIM4ZYOpeVQrYrcdACBuxyGad7F84LAUV1JUjJvedEGh1WkpKS4BoPcDA3jhh9e4f/aDdijB36PX6Vi3ehnBgXUcnUq1tG3aUbxvDwDF+/ehbX2TxTjDxMnkfbTiynpt26MJi8D79WUoBk/U3By75GtJ/ahITm0/CMCZHbHU79LCqpiTW/bzx8r/omhc0Hl7UGhueLsZ9KaVFIUQB5wEAAK6RpL6mynf1O2xBERHVogxFhSz9ZanKUhMu2a5q+fl/MEn0jH5AzSMiuTkNlMZTu+I5QYL28VSTElhMUsHPEvWVeVqGN0Sd28DD34+i/odm5J+7qJ9CmFWP6oFZ7YfAiB+x+FK6pjlmD6zR7Hl1S/K4kqLitG6u+HiqjEtUG2cfCWCo1uQ+Jsp3+SYwwRHVSxTaUExP/SZQV5i+jXLO8wbxb6XvqgQ7wiNo1py1FyHju84xI0Wtk1lcXFb/2LLez/gonHB3duDwux8i8vsISKqBfHbTNvjXMxhIixsD0sx9aIj0fsYuOfL5wjr1JTMsxdxcdXQ7dl7iVn4lV1yr0xodAsumPNNjDlM3Urq2Lq+M8hLulLH8lMziX3/ZwCMRSX2SbYaodEtSDCX5XzMYUIrKctXfWeQe1VZLC1zlAblzoMNLOwrlmJyUzP544OfAMp69k9s2c9O8/lTf9X5056aRkVyxHzNeGxHLE26VDxPVhU3bPYYvn1lbVlcs+iWBDUK5cn183H3NpCf5Vw3423CaLT9y0lVOSRPURRv4H7z209UVc02L39GVdUaG1cxat54wptduVArLSkt6wUqyMmjbqNQi+t5+nlViPt9wzZmfrOAjoOiOBxzgItnk4k/eJKEI/Gc3Hec17Yt5ef3v+NSgn0voGoLxcsbNS8XADUvFyU8okKMW48+lJw+SenZ+LJlLj6+lMafJue1efi+/xkF67/AeDHFbnlfzd3Xk0Lzga0oJ5+AhnWtiinOKwRgzPo55KRkkHHuIofWx9CweyuGvjuN0qJiXPVu9ivIVdz8PCk251uSnY9no4plsiTh6xgCe7ahw/vTMRaVoHFQ/gAefp4UlO3P+QRYKIM1MQAGfy9y07JZO/51xn/zAvU7NiV+91HbJV+Ou68nBebtUZiTj38ldax8TIs7u5ASd5ZLx8+Xxe354CfGfD+fLo/ewb5PNlGcX2ifQpSj8/Ok2PzZF+fk421lHWswpAvph8+Seex89cE2MHTeg4Q2q1f2vrSk9Jo6FFTJ+cVQrq4FNQqlyHwMmLZ+Plkp6Vw6d+UYZmmZLbn7eZZddBbl5ONnYXtYiinKKSDvUhbrx73OfetmE9apKWEdm3L46+3kpWbZJffK6Hw9KbrquOtjZR3LOp0MQP1bO+Di5sr5LQdslqO19FeVpTgnH18ry+JMPK46DxZWcq60FJN2xrQ9mvbvgEar4eTWA6hG052ecevnkG0+f9ra3fPGEVZu388339DIr3Lf96oQ1+GOaM7HxZN04spxzNPfm8Sj51g1ZQlztr3Npve+J/3CJRuWSDhSdXOYNgBngCLgIUVRBqmqmgzVVRgYAAAgAElEQVQMBCo0mBRFmQBMAOjifxNNvW6wKok1s64dyzrhjam4e5m6Nt29PMhOy7a4XnZaVoW4QY/cxZZPfuG3z39l4pLp3NiuKSlnk8hOy0Y1GklPuoR3gI80mP6f1KxMFA8DAIrBgDErs0KMW6cuuAQGoW3XEU14PfS3DUHNy6U04RwYjRhTU3Dxr+OwBlN+ejY6c9e5zsuD/PSK9ctSjLuvJ0V5BXx01wuM+HQm9bu0ICn2DD88tZK8tGzuWjqF3NSKn4c9FKVlozXn6+rtQVEl+4wl+x9/l6JL2bRfOZVCB14w5aZlozfvz3ovD/IslMGaGDBd4F46lQhA2tkUvEL8bJS1Zfnp2ei9L+fpXmkdKx/TqPdNeIcGcEP31gQ0qku7B/oS0rIBP834gLM7D3PfpzM48t3vZDngpFyYlo3W/NlrvdwpsLKOhfW5CUNYAHV7tMa7UV2ajO3LsQ832jLVa3w964Nr3o9847GyOuTu5UFuJeW4uq5djvPw9aQwr4A375rFI5/O4sYuLbgQd7bCshM7D9u2UEB+WjY6c35uXu7kWyiHpZiinHzSzftG5tkUPIP9uKFna1Sjis7bA49AHxoP7Mjx/+62eRnKK0jPxs37Sr7W1jGAen3bETmuPxvHLCq7OHekf1IWZ5FX7jyYZ+E4VllMkz7t6Dy2P5+NM22Py+fPD+56gdGfzqRBlxacsfF+8sWs9695/8Abk3H3cgcu79OWz3c5adkV4lr2bodfaB2ad29DcMO6dB/dn4KcfJJPXUA1qqQnXsIn2L/2N5icuAfI1qobkueuqupYVVUnAo8BGxRFqXSSg6qqK1RV7aCqagdrG0uWxMUcpGV307yl5lGtOLLzkNVxeoM7xYXFAJQUFaMz6Ln3uTE06dgMrc4N/9BAkk8n/r9z+7cr3r8XbbuOgGl4XsmBivOucl6bR9ZTk8l5ZS4lJ45S8P06Sk4cw7VJU3BxwSUwmNKUJHunXuZMTGzZWOwGUS04s6PiQdtSTOfxA2k+qDOqUaWkoAhXvZZ6nZoxYMGDaNxcCWpej/P7Tti1LJelbo8tm7dUp2skqTHWnYgCbm5O61fG4eLmineL+qT/edyWaVbpVEwsjczzqxpGteC0hZOpNTEAiYdOE9q6IQABDYJJP2vfxnl8TCwNzPWnXlQk8RbqmKWY76Ys5ZNh8/h28tskHTzN3o824mbQU1pYbLoIVE2TrB0haXssoeY6FhIdSbKFMlkS8+hSfhk8j+2T3ibt4Gm7NpYsORZziKbdTXMSGkdFcmJnrNVxPcffRtuBN6MaVYrzi9Dq3Cwus4ezMbHU736l/pyzsC9Yikk+eJrgNqZ9w7dBMJlnU/h8+Hy+uOdFNs/5mNO//uWQxhLAhe2xhJnzDY2OJNHKOuYe6EOrhwfxy+iFFOcW2DJFq53fHkv4VWW5YGVZnMnpmNiyuaQ3VHKutBRjCPQhauIgPh27kCLz9ugyfiAtzOfPYvP5096OxhykmfmasUlUJMcqOX9Yils19S0WD5/Nh5Pf5OzB0/y2+mfOHjpF/daNUFwU/EMDSJMb8bVadQ2mzYqibFUUpZeqqr8DUzH1Otl0osPODb/hG+zP3B9fJzcjh8MxB6gTHsQ9M0ZXG/frmh/pNbI/M79ZgJvejcMxB/n+nW8Y9vRIZnw1n2+XfEleVq4t06/VCjdvxCWgDj5vf4CanU1p4nk8xk2qdr2imK24BIXgs/hdCjf9gpqeVu06tnJofQxeIf489NNL5Gfkkn42hd4z768y5kxMLH+u3kibu3vwwLoXyEvP5tTWA5zcsh9XnZbRXz5PzFvry4bt2dv5r7ejr+tPj02vUJyeQ258Mi1mj6h2vZRNf+GidyNqw2yOv7GOUgflD3BgQwzewf488uNL5GXkkhafQv8Z91cZcyrG8sXuub0nyEvPZuKGuaSeTOT8/lP2KEKZ2PU78Arx48GfFlCQkUPG2RR6zbyvypj4Ssqyc+l33DJrBKM3zCH5cDzppx1zs+H0NztwD/Fj0P8WUJiRQ/aZFNo9f1/1KzqZPzdsxyfYn//8+Ap5GbkcizmEf3ggd8wYWW1czOqf6Xx3T6Z+M5fc9GyO/Lbf4jJ7iFu/A88QP0b/vICCzBwy4lPoUa6OlY85uz2WxL0nyE/LZsR3c0k7lUiSnfeNqpxctwOPED+GbDTVsaz4FDo9V30dazy8Gx7BvvT/5CkGfTOLxvd0t0O2VTu+bgeGED+GXVWWm60oizM5aD4PTrzqXNm33LmyfMzpmFjaDO2GZ5AvI9Y8zZivnqft3T3YvXojbe/uwYPm8+fJrfYfNrlnw3Z8g/149sdXycvI4WjMQQLCAxlSbt+3FGfJXz/+gX9YIE99+xK71m0jy/wk5tpMVUtt/nJWiqpW3XWtKEo4UGweioeiKDrgTlVVq5zBO7bBUMf3ideQFXtec3QKNSbrgbGOTqHGLD8U7ugUakzrwtrTzb1b75TPkvl/8VAVR6dQY8KLHZ1BzfnTzTkm9teEcKNTfrvH/4tv7TmMUXtqGCRras3lGClK7dkyb5/5/Lo7weRv+cDmlcm954NO+blUe2WjqmoCVx07VFUtxGHPaRJCCCGEEEII+7H2VnD5b7OcUtOJCCGEEEIIIZyUarT9y0lV91jxHkBPoJ6iKM+bF3sCjpuAIoQQQgghhBB2Ut3g6TPAFmAosNW8LB+o+Gg0IYQQQgghRO30L36seJUNJlVV44F4RVE+UFV1a1WxQgghhBBCCFHbWPt4np8VRXkGKPuCCVVV59omJSGEEEIIIYRTceI5RrZmbYPpK+AFwL7f/iiEEEIIIYQQDmRtg+kisE5V1drzAHwhhBBCCCGEdWQOU7X2AFsURVkL5ACoqrraZlkJIYQQQgghhJmiKHpMo94igAPAaFVV1XIxBmAtUAeIUVX1qZr429Z+D9NBYCWmxpJifgkhhBBCCCH+DRz/PUwjgQRVVdsAfkBfCzEjgN9VVY0GIhVFaV4TRbe2wbQW0AHtAC3wSU38cSGEEEIIIYSwwi3ARvPPm4BeFmIKAQ9FURRADxTVxB+2tsH0IVAX+BEIA1bVxB8XQgghhBBCXAeMRpu/FEWZoCjKnqteE67KIADINP+cBfhbyHItMACIA46oqnqyJopu7RymBqqqjjT//LOiKNtr4o8LIYQQQgghBICqqiuAFZX8dyrgY/7Zx/y+vGeB5aqqvqcoyqeKokSpqrrjn+ZlbYPprKIoM4GdQBfgbHUrrLnw+z/Jy6ksfGCso1OoMd4ffejoFGrMsiZ3ODqFGvOMoa2jU6gx72fuc3QKNWaW502OTqHGrNZYOq9cn7Ylxzk6hRoT4uHr6BRqzFjPlo5OocYUKmr1QdeJRUm15x73hJAujk7h383xT8n7FegHfI1peN5iCzFeQIH550LAsyb+sLVD8h4GNMAwTF1gE2vijwshhBBCCCGEFT4BwhRFOQCkAScVRVlYLuYdYJKiKDsBd0yNrH/M2h6m1cC3wDxgJqY5TcNqIgEhhBBCCCGEk6v+KXa2/fOqWgjcVm7xk+VizgDRNf23re1hClJV9QPVZD4QXNOJCCGEEEIIIYSzsbaHKV5RlKeBXUBn4ILtUhJCCCGEEEI4FcfPYXIYa3uYxgB5mIbh5QKjbZWQEEIIIYQQQjgLq3qYzGMG37JxLkIIIYQQQghn5OA5TI5kbQ+TEEIIIYQQQvzrWDuHSQghhBBCCPFv9S+ewyQNJiGEEEIIIUTVZEieEEIIIYQQQojypIdJCCGEEEIIUbV/8ZA86WESQgghhBBCiEpID5MQQgghhBCiav/iHianbzDpdDq++GwF4RGhHDwYx5ixU6qMnzplPAMH9Kb/gHuZMH4Uj0+fSHLyRQBuv3M0WVnZ9kj7Wlo3vGbMwaVOEKVnTpGz6MVKQ/WDh6PteDPZM58AwGPcI7g2b0np+bPkLn7ZXhn/I8UlJUybMY93Xp3j6FSsotO58e6qxYSGhRAXe4ypk561GKfRaHjnvVcJDgni5IkzPDl5Vtn/PTRpFL37due+u8bbK21TTjotA5ZPwTPUn9S4c/xv2nKrYlzddfR7+xHc/bxI3HOMHQs+wysikP5vPwqqSuynm4n7/De7lqU8nc6NFR8tJjSsLnGxR5nycOXbZel7rxFcN4iTx0/zxORZ9OzdlYVvzuHcuQsAPDl5FidPnLFb7hqdln7Lp2AI9Sct7hybKtku5WPcfDy4deV0XFw1nN16gL1vrsfVXUdv87ZK2nOM3xd8ZrdyWKLVaZm1/DkCQwM5HXeaV6e9VmmsxlXD7BWzeP7BFwDw9PFk9srncXXVsGfrn3zy5lo7ZW2ZTqdj7dplhIfX5dChI4wbN73K+MmTx3HrrbcwaNAI9HodH3+8lJCQQP76K5bHHrNcP+3FTefG0g8XERoWzJHDx3l80kyLcRqNhiUrXyYoJJBTJ+J5espsi8vsSaPTMnzZVLzr+pNy5Bzrpy+zOubORRMJaBhK7qVMvpj4Bg27tuT2V8aTkWA673/31EounUq0af6uOi33LZ2KT2gASXFn+erxivlbiqlsvW4Tb6NZ73YU5RXw8fhFlBaXonXXMWzRw3z6yJs2LUtVdDodn366vGx/efDBaVXGT548jgEDejNw4P3o9To++WQZwcGB7N8fy6OPPmOnrC1z1Wl5cOl0/ELrcCEunjWPv/O343qOG0hkr3a8M3K+vdIWDuT0Q/JG3H8XCecTad+hL36+PvTt073S2Hr1whg9+u5rls2Zt4gevYbQo9cQxzSWAF2vvhhTL5I5eRyKpyfamzpYjHMJDEbX+9ay966tb0ItLCDryUcwJiehGDztlfL/W0FhIXc/OJmdu/c5OhWr3XX37SReSKZf96H4+HrTvVeUxbhbB93C4UNHGTJgFMHBdWjRsikAYeF1ufu+wfZMuUzTIdHkJKbxWf+Z6H0MRHRvaVVM0yFRJO89wdd3zcW/STh+N4bSanQfTv53N18NmUur0X1x1bs5oERXXN4ufbvdVc126c3hQ0cZfOtIgkICiWzZDIDVH37OkAGjGDJglF0bSwCNzZ/5V/1novMxEG5hu1iKaTw4irRjCay/ay4hHZrgFRFI4yFRpOw9wfq75uLXJBzfG0PtWpbyeg/pTWpiKpP6P4Knjyftu7ezGOemd+Od/75Nu25X/r/X4F7EH4tn+l1P0KJDC0Iigu2VtkX33TeE8+cT6dx5AL6+PvTu3a3S2Hr1whg5cljZ+8GDB3Lo0BG6dr2D3r270axZY3ukXKkhwweRdCGZgT3uxtvHm249u1iM6zewF3Gxxxg+cAxBwXVo3rKpxWX21HpINFmJaawYMAO9j4GG3VpZFRPRoQkurho+GDIbnac7jbqb1tvz8f9YNWwuq4bNtXljCaDt4GiyktJ4e8CzuPsYuNFC/pZiLC3ziwgiqEk4K++ey7Et+/EOCcAvPJBJG+ZRt0V9m5elKvffb9pfOnW6FV9fH/r0qXp/GTVqeNn7IUMGcuhQHF273k7v3l0dvr90HNyNjKQ0XhnwFO4+Bpp1a/234vzC6tB5WE87ZuwkVNX2Lyfl9A2mXr2i+d+vpjvdm7fE0LOn5YsmgMWvz+W55166Ztmjk8aye9fPvL7Icb0d2jbtKN63B4Di/fvQtr7JYpxh4mTyPlpxZb227dGEReD9+jIUgydqbo5d8v0n9Dod61YvIziwjqNTsVp0t05s27IDgJhtfxDVtZPFuM2/bmfF0tVoNBq8fbzIyc4FYM5Lz/Dy3Dfslu/VwqNbcG7bIQASdhwmvEsLq2JKi0pwddcBpju3pcUlAGg99SguChqdFt9Gde1UCsuiu3fmt807AYj5bRdR3SrfLu8u/QiNRoOPtxfZ2ab9ZODtffn+f5+x4iP7b5uw6BYkmD/z8zsOE2phu1QWozXoAVAUCIisX2FbGc3bylHaRrdh77a9AOzfsZ82XdpYjCsqKOLhfpO4mJR6zXJ3gzsAiqLQKLKRbZOtRs+eUWzatB2ArVt30KOH5UYGwMKFs3n++VfL3h89epy1a78BoKioyLaJWqFLt05s32raX3Zu28XN3TpajNu6KYb3lq656jiWY3GZPTWIiuTU9oMAnNkRSwML+4ulmNzUTP744CcASotLy2KbD+jIuA1zGb58qh2yh4ZRkZzYZsrt1I7DNLSQv6UYS8saRUfi7m3goc9nUb9jM9LPpZCecJEl/Z6yS1mq0rNnFL/+ug2ALVti6NGj8uuxhQtfYNasV8reHzlygk8+uby/FNs2USs0jork6LYDABzfEUvjLpF/K27o7DF894pje8iFfVU5JE9RFC0wAMgAtgGjAXdgtaqqebZI6K0lC2jdqnnZ++LiErIyTT1DWVnZNGli+QR7772DOXDgMIfjjpUt27v3AIcOxfH7H3s5cex33lyykvj4BFukXSXFyxs1z3RxreblooRHVIhx69GHktMnKT0bX7bMxceX0vjT5Lw2D9/3P6Ng/RcYL6bYLe/a6sXXnqN5ZJOy9yXFJWRlmS4QcrJzaXRjA4vr5eXmA/DdxrUkJ13kbHwCg4cOJC72GMeOnrR53pbo/TwpzDbtikXZ+fg2rNjIsRRzbP0Ohm2YzY2DOnEuJpas+BQOrvqFW14bT+9FEyjMzLV7D9OChbMqbJdsc69wdnYOjRo3sLheXq6pbN9t/JSUZNN20WhceG3BW/z6y29s+PljukR3ZGfMbpuX4TK9nydFVmyX8jF/LfueiB6t6bdiqqmhpNdyYv0OBm+YTcNBnThv3lb2NPnFR7mhWcOy9yUlJeSa887NziO8YbjVv2vTuk106NGe51fMorioGDc717E33phPq1bNyt4XF5eQmZkFQFZWDo0bN7S43j333MmBA3HExR0vW7Zvn6mx+9hjD7Jv3yGOHDlucV1bmfvaDJq1KL+/XD6O5dCwmuPYul8+JiX5Iufiz5f9n6Vl9uDh60lhlqlOFebkE2Bhf7EUk3YmGYCm/Tug0Wo4ufUAfvWC2LLoK45v+oux38ym/s3Nif89zrb5+3lRkJ1vzi2POhZuNlmKsbTMkFNAbloWH49fxMRv5lC/Y1Pidx+1af6VefPN+bS65nqsuGykTnZ2TqXXY/fccycHD8Zds0/s22dqGD722Dj27Tto9/1l+LxxhDarV/beWFJKvvmzL8jJJ6iR5Z57g59Xhbj2d0RzPi6epBP23U+cgsxhqtQXQCJQB1gEHAGyzctvKx+sKMoEYAKAovHBxcXwtxOaPGXGNe9Xf/QW3j5eAPj4eHPpUprF9QYN7EO9iDD69e1JkyYNeWTSGL76+ntSU9MwGo2cP59IUGAdhzSY1KxMFA/TZ6EYDBizMivEuHXqgktgENp2HdGE10N/2xDUvFxKE86B0YgxNQUX/zrSYKoBM/9z7Xjjt959GW9v03BHL29P0i5lWFzP18+HvNw87uw/ki82vE9U14706d+D0PC69LglioY3NmDMQ/ex6r1PbV6Gy/LTstF5eQDg5u1OQXrFYaeWYto/ejuH1mzi8Gdb6Pf2o4S0b0zG6SR+fHgJRVl5DP9+LvmXsuxWDoAZT8675v1bK17By9u073t7e5J2Kd3ien5+PuTm5nFn/xF88e0HRHXtRFzsUbZtMd1tP3f2AnUC/W2bfDkFadm4VbNdKovZ8uRKCtKy6btsMvmpWdz06O0cXrOJI59toffbjxLcvjHJf9rvYuOtmdeO7X9myVMYzHkbvD3ITK94PKvK608uJjMtk+eWzSQj9e+t+09Nm/bcNe8//PBNfHy8AfDx8eJSJXVswIDeRESE0rdvdxo3bsTDDz/A8uUfMX78SKKjOzFq1GM2z7285/+z4Jr3i5cvwKvsOOZFeprlslw+jg29dTRr16/k5q4dORJ7rMKy37fb7wZDXno2Om9TndJ5eZBnYX+pLKZJn3Z0Htufz8YtQjWq5Gfkcmq7qTGbkZCKIcDb5vnnpmWj93K/kltaxfwtxbh56CssK8zJJ9U8jDDtbAreIfY9dl1t6tRr95dVq97Eu+yY7FXp9djAgb2JiAijT5/uNGlSfn/p6JD95ctZ71/zfvQbk3E3f/Z6Lw9y0iyf73LTsivERfZuh19oHZp3b0NQw7p0G92fbat/tm0BhMNVNySvjqqqj6iqejdwUlXVUaqqPgL4WgpWVXWFqqodVFXt8P9pLFmyadN2+vbpAUCvntFsMQ+dKm/U6Mfo0WsI94+cxN69B1m6bBULX5tN1+hO6PV6IiLCOH7idI3k9HcV79+Ltp1peIS2TTtKDlSc35Pz2jyynppMzitzKTlxlILv11Fy4hiuTZqCiwsugcGUpiTZO/V/he2//VE2Pya6W2d2bN9lMW7io2O47c7+GI1G8vML0Ov1PDbhae4aOJpHxv2Hg38dtmtjCSAhJpYI87j98KhIEnYctipG6+lOSaFpWERpUTFag44md3ah49TB6P290HroyDTfvXWU7Vt/p8ct5u3SvTM7tlWyXR4bw22Dr9ou7jomPPoAd941EEVRaNr8Ro7EnbBn6td85mFRkZyvZrtcjqnbuRndXxqLi5srAS3qkbz3BFpPd0rN28po3laOtC/mL9p3bw9A26i27N9xwOp1W3VuxZSXJqN109KwxQ3E7bXtnf/qbN4cUzZvqUePKLZutXx+GTNmCr17D2PUqMns23eQ5cs/olWr5tx66y2MGPEIJSWOHSYJsOO3XXQzD1nv0q0jOytp8Dz06GgG3tnvquOYzuIyezodE0sj87yfG6JacMbC/mIpxhDoQ9TEQXw6diFFuQUA3Dx+AJG3dwFFIahJOCnHbH+T9FTMIW7sbprb0igqklM7K+ZvKcbSsvOHThPW2tRzE9AgmPSzznOTdPPmGPqY55H37BnFVvMQ0PIeeGAKt9wylNGjH7tmfxkwoLfT7C/HYg7SrLtpOHGTqEiOW9hmlcWtnvoWbw6fzarJb3Lu4Ol/V2PJaLT9y0lV12A6qCjKCkVRDKqq3qsoioeiKDMxDdGzi7WfriMsNIS9f24kLT2DXzdto0GDCF59eVa16778ylsseHEGW7esY/6Li8nIsO/dzMsKN2/EJaAOPm9/gJqdTWnieTzGTap2vaKYrbgEheCz+F0KN/2Cmm75bo74Z9Z9+T0hdYPZuO0bMtIz2b71dyLqhfHc3Cevifvo/U+5Z8QQNvz8MelpmWzZFOOgjK84um4HniF+3PvLAgoycsiMTyH6ufuqjEnYHsvBjzbSalRvhq2fjavejYTtsRz9ZjvBbRtx26on2DZ7jYNKdIVpuwSxcbtpu2wzb5dZ5bbLqvc+5d4Rd/Htz5+QnpbBll9j+HDlWu4eMZjv//cZP33/K8ftPGTy+LodGEL8GP7LAgozcsiKT+HmctulfMz57bGc27wfjc6NwV/PYu+SDZTkFRL70UYiR/Vm8PrZaPRunN8ea9eylLdp3WYCQgJY/ssysjOy2bd9HyERwYx/7qFq1929eTduOjcWfb2QtUs+pSCvwA4ZV+6zz9YTGhrCrl0/kZ6ewebNMdSvH8FLL1l+wtzVHnpoBPXrh7Nx4xf8+utX9OvX0/YJV2HDVz8QXDeIH3/7ksyMLGK2/kF4vTBmzHn8mrg173/O8BGD+fqn1WSkZ/Lbph0Wl9nTwfUxeIX4M/Gnl8jPyCX9bAp9Z95fZczpmFjaDO2GZ5AvI9Y8zZivnqft3T3Y/dFG2g7vzrgNczjy8x5Sj9t+2NRfG2LwDvZj8o8vk5eRQ1p8MrfOuL/KmJMxhywuO7f3OHnp2UzaMI/Uk4kk7HfMcG9LPv3UtL/s3v0z6emZbNq0nQYNrNtfxo8fSf364fzvf1+yadPXDt9f9mzYjk+wH0//+Cp5GTkcizmIf3ggd84YWW2c+HdS1GqeSKEoSnvgkKqqhYqieAP3AJ9UN4fJ1S3MeR918Tcl973R0SnUGO+PPnR0CjXmhiZ3ODqFGvOMoa2jU6gxL+VcP09IrM4sT8sPaLkefUNq9UHXiW2pju2ZqkkhHhYHbFyXxnpWfBrk9apQqTWXMCxK2u7oFGrMhJDKH8xyvVly5nPF0Tn8Xfkfz7T5juE+8kWn/FyqfUqeqqp/Ap7mn7NUVV0JDLJ1YkIIIYQQQgjhaNY+VnxdufdVf3usEEIIIYQQovb4F89hqu6x4j2AnkA9RVGeNy/2BGQyjRBCCCGEEKLWq+6x4meALcBQYKt5WT5QeyYpCCGEEEIIIapWzXMParMqG0yqqsYD8YqifKCq6taqYoUQQgghhBCitqmuh+mynxVFeQYo+1p2VVXn2iYlIYQQQgghhFNx4jlGtmZtg+kr4AXAeb5BTQghhBBCCCFszNoG00Vgnaqqjv96ZiGEEEIIIYR9SQ9TtfYAWxRFWQvkAKiqutpmWQkhhBBCCCGchyoNpuocNL9UwCm/gVcIIYQQQgghapq1X1y7FtAB7QAt8InNMhJCCCGEEEI4FdWo2vzlrKxtMH0I1AV+BMKAVbZKSAghhBBCCCGchbVD8hqoqjrS/PPPiqJst1VCQgghhBBCCCcjD32o1llFUWYCO4EuwNnqVjjevMU/ycupLD8U5OgUasyyJnc4OoUac/rYt45OocasbzXL0SnUmNVuLR2dQo15zyXH0SnUmK/61Z4T3YWtkY5Oocbk5WsdnUKN2V9Se6Y4K7VouvZnvl0dnUKN2VuLtou4vlg7JO9hQAMMA7KAiTbLSAghhBBCCOFcVKPtX07K2gbTaiABeBTwwTSnSQghhBBCCCFqNWuH5AWpqvqB+ef5iqJss1VCQgghhBBCCCfjxE+xszVrG0zxiqI8DewCOgMXbJeSEEIIIYQQQjgHa4fkjQHyMM1hygVG2yohIYQQQgghhJMxGm3/clJW9TCpqloIvGXjXIQQQgghhBDCqVg7JE8IIYQQQgjxb+XEPUC2Zu2QPCGEEEIIIWOJN+4AACAASURBVIT415EeJiGEEEIIIUTV1H/vU/Kkh0kIIYQQQgghKiE9TEIIIYQQQoiqyRwmIYQQQgghhBDlSQ+TEEIIIYQQomrGf+8cJqdtMCluWoIWPY8mOJDi46e5OPOVikEaF4JenokmMIDi+HOkzl6EvkNr/B4bC4Br3SDS315Fzncb7Zw9aHRahi6binddf1KOnOPb6cusilE0Lgx56zE8g/1IO3WB7/+zEr23B8NWTMdF68qprfvZvmS93ctTnk7nxrurFhMaFkJc7DGmTnrWYpxGo+Gd914lOCSIkyfO8OTkWWX/99CkUfTu25377hpvr7T/tuKSEqbNmMc7r85xdCpVctFp6bJyKh6h/mTGnWPX5Ir1DUBx1RD1/jRiHlhUtqzpI7dRt99NlOQWEjNmEWpxqb3StshFp6XV+9PRhdYh53A8hx97x2JciyWP4HFjKEWpmRwcuwi11GhxmTPQ6rRMW/YUAXXr8H/s3Xd0FFX7wPHvZFuSTe8JVQKELgKhJBCQIoKodLuiKKiIgvKKgFgACyj8rNhRsLx2QEXs0gKKSA89QEIgDdI2dZPs/P7YEAm7SdbXZHeF53POnpO9eSb73Oy0O/femdQDKSyd/kKtsRqthulvPMLzE5+qLrtn8f1EtmpCwZl8lkx+Fosr6qXV4XXPXDwCQ6lMO0bpskU2IZqOPfC6bTqW05kAlC5fgiX7FF53zUYJCMaScYLS5UucnbkNRa+jyctz0EaGUHbwOOn/ed42SONB1JKZaMOCMR9LI2P2C/bLXEwx6Gj1+kx0USGU7D9OyjT7ObVY8gCe0U0oP5PP0buewUOvo+UrD6EN9KNo235OPr3cyZnb0hh0XP7m/RijgsjZf4KN979uN07Rahj49gP8POGvdanfC5Pxi46k9HQBv9z5gsu3fY1Bx4CquuT+jbooGg/6L52CV3ggBcnpJD70ljPTtsvDoKPn2w/gFRVMwb5U/qzj+NJr2XR+u9W6PWm8DfRYOgV9kC85fxwiaf5/nZl2DVqDjuuWPoB/VBAZ+0/w5YO2dagtxkOr4YbXp/HhndbjZsve7Rn00DgAApqE8PPiz9j5xUbnVUa4hNsOyTNeNZiKzNOcGn83Hn4+ePXpbhPjfXk85kPJpE+YhiYkCH1MNKXbdpM+YTrpE6ZjPnSMsgNHXJA9dB4Vjyk9h7eHzcbT38gl/To7FBMztAeZ+1NZMeZJfMICCe/Qgo4j48k+fJIVY56kafe2+DcLdUGNaho9/mrST2VyRcIY/AP8SLg8zm7clVcNZN/eg4wadgvh4SF06BQDQJOmkYy/YaQzU/7bSsvKGH/HVLb8scPVqdSrxZh4StJz+HHwbHT+RsL7265vHp46Bn+/gPCETtVlxuah+MU0Yd3I+WT8sgvvyCBnpm1XxNh+lJ7KYevAh9EFGAnq38Umxr9nDIpWw7bhj6L18SJoQBe7Ze6i76j+5KSf4ZFh0zH6G+ncr6vdOJ1Bz9PfLKZz30ury2J6tMdDq+GxUTPx8vGmS8Jlzkq7Zm69B6HmnqZo3j0oRh80HWz3yQDmdd9QvOhBihc9iCUzDW3XeCrTjlK8cDoe/sF4NGvl5Mxt+V07kPKM0xy/5j40fj54x9v+T30H96HswDFSb5iBNiwIQ/tWdstcLWjUAMzpZzgwdBpafx98E2zXLWNsexSthoPXPozGxwu/hMsIHNWfou0HOTT6ETzbNsezdVMXZF9Tq9HxFKXnsHrIHAz+RqLO2VedpfHUcc3a+UT1++t3YbFtUTQerLn6CXQ+XjSxs/9ztlaj4ylOz+GrIXPQ11GXq8+rS/Mru5OzL5W1I+fhFRZAUMfmzkzbrmZj4ik5lcOvg2ahCzASWsvxZcAPTxF6Tj2bjo4n588jbLzmSXxjmuLTJsqZadfQZWQ8BRk5LB02Gy9/I9F2zsnsxWgNOu7+egGtzvmOjv+2n3fGzeOdcfPIPJBKetJxJ9bExVRL47/clNs2mLx6dqVky58AlGzdiWfspTYxJYl/kP/+F6DxwMPXB0thUfXvFE8D2uZRlB8+5rScz9UiriNHN+0B4PjmJFr06eBQTPK6Xfz+1rcoGg8Mft6UmYoB0Bs9rQspChEdWjinEnWI79eTjes2A5C48Xfi+va0G/frz5t4c+kKNBoNfv6+FJqs39GTzzzCs/Ncf3W2Lp4GAytXvEZ4aIirU6lXWHxHMjdY16WsxCRC423XN0tpOT8OmkVJes5fy/XrhN7fyICVcwnpFUNRarbTcq5NYN+O5KzfDUDOxiQC+3a0iTFn53PirW8BsJRX1FrmLjrGdWHPpl0AJG3eQ8c+tidPAOVlZmZeOY2cjDPVZfmn8/hu2TcAVLiwXtp2XanYtx2AygO70MbY7pMBdN36Ypz9El53W3uTK5L+wPzjF+DhAd5G1JJip+VcG+/el1KcaL0QUvTbLoy9betSuPFPct5dCRoPNL5GLIXFdstczTe+M6aNOwEwbd6Nbx/bE8GK7Dyyln0NgFq1Dqnmcjy8DIC1l8odtpnI+A6c2rAXgPTEfUTG2e7HKkvLWT1kNsXpudVlpafz2ffO94D7bPvn1yWilrp8NWQ2RefU5eSvu0l6Yy2KxgO9vzdmU4nTcq5NSN+OZFcdX7I37av1+PLrwEdqHF8s5nK0VeuYxqDDYnbdd9MqriPJG611OLY5iUvsnJPZi6koK2fpsFkUnFOvs3SeeoJahJN54ETjJu9OLGrjv9zU3xqSpyjKPFVVH2usZM7lEeBX3QCyFBaja2l79UstKQUg8oOXqMzOoeJkRvXvvHp3o/R31/UMeAX4UFZgPZiaC0sIbhXpUEx5cRkAE1Y9SWFWHnknstm7KpFWCZ0Z88Y0Ks3laD31zqtIlaeee5T2HdtWv68or6CgoBCAQlMR0a1b2l2uuMi6s//6x4/IzMgmNSWNkWOGsz/pEIcOJjd63hcLfZAP5VXrUoWpBN9o2/XNHkOwL2VnTCROWMLAr58gpFcMp38/2Jip1ksX6EtF1UlCZWEJuta2VyVLjmVQAoQOi8VDp+XMr7vAotqWucgdCybTrN1fFzYqKyopLrDuz0oKi4ls1cThv5VxPB2AHkN7odVp2bXeNfs1xccPtcRaB7WkCI9wO/vk7FOUrV5OxZ6teM/8PzRtu1B5yNr4Nc56CUt+DurpDJvlnE0T4EvlOccXj0tsvw+12Hp8afHZ/1GRnUP5ib/ytlfmKtpAPyqrLqxVmkow2Fm3yqrWIf8re6PotBSs3wEeCjGrFxF4VTymxN2YU1xfF89AH8xVdTGbSvB3cD9WcMw6BLT5lT3w0Gk5uW53o+XoKMM5dSn/G3WpqDoHuOrrJyjJyqPQDS5i6QN9KS+w7pMrTMX4OFiXtC83k7DmSaKu7kX2pr0Up2Q1Zpp18g70obTq+ygtLCHYTh0ciTlXdL/OHN2c1PDJCrdUZw+ToiiHFUU5VPU6DDx0tqyW+EmKomxTFGXbf8+k/aPELLn5ePgYrUn6GrHkFtgm7+8LOh3pt07Dw8+3Ri+Ud/8+FG/4/R/l8E+U5Jow+HkDYPD1piTX5FCMV4APGr2W5aOfwNPfWN0ztebht/hi8gtUmisoOp3vvIpUmfOfBYwefmv1KzMjCz8/HwB8/XzIOZNnd7mAQH/0eh3XDr2ZgAA/4vrGMnhof+ITerH0nefo3LUDE+68wZlVuSCV5ZjQVa1LOl9vzDm265s95aYSTMnWk6nC1Cy8IgIbLUdHleeY0Pp6AaD19cacY7vtA4QM7U6zu4ax65ZF1Vel7JW5wrJH3+DJsbOrX7mZOXj7Wfdn3r5GTHb2Z3XpPjiWYbdfzXMTF6C66LauamEBipe1DoqXEbXQdj+kFpmo2G9t0KlnMlH8AlCMvqDVUfTsNBRvHzS19Ew5U2VuAZqq44vG15tKe8eXAF8UnZaU6x6yDtvr1cVumatV5BSg8bVu+xo/byrtHGsA/If0JOyOESTf/hRYLERMGcvp979j3+VT0AT4YOzezplp21WaY0JfVRe9nxelDu7HAJoN6UaHiUP5acJiVDe4Sv2/1sUQ6IOHXsu31z6J3t9IRFz7xkzTIeYcEzo/6z5Z5+f48aXt/ddwfPlP/NxvBvoAH4J6tGnMNOtUlGPCs+r78PT1pthOHRyJOVfMoMs4+LP7D9lvSKrF0ugvd1XfkLyHgXTgblVV2wBbVVVto6pqW3vBqqq+qapqD1VVe9wQ/M/GQ5ds3YFXnHWMvFdsV0r+2GkT43/rOIxXJIDFglpaimIwVP/OM/ZSSre6bkU+nphEq6oxsi3jOnB88z6HYnrdNZz2V/VCtahUlJrReupo3rMdw56+A41eS1j75pzc4Zp5WefatOH36nlL8f16sXnTVrtxk6dMYMS1Q7FYLJSUlOLp6cl9k2Yyevit3DvxP+zZuY/33nbdRNALRdbGpOp5S2F9O5CVaLu+2ZO7+xiBXa3zMHxahlPowiuAZ+Vs3EPwAOtJdWDfjuTaqYs+1J8W917NzpsXUllUWmuZu0hK3F09b6ljXGf2bd7j8LL+oQGMmDyKRbfPp9SF9arYvwNt1bwlTbuuVBy07cHTDxmDLnYAKAoeTVpiOXkc/RVj0XVPsI5NN5eh6JzfQ36+4i078e5rnbfk3ftSin+37ZEIumM0vsP6gcWCpbQMxVNvt8zVTIm7quct+cZ1wWRn3dKGBhB+90iSb5uPparXX+PjhVpmBqqG550d9u1C6ZuSqucfRcZ3JN3OcdMer1B/Ot1zFT/d+jwVbrLtp29KIqqqLhHxHclwsC4dJw+n5Yiqc4ASMxo3WMeyN+0ltGouaUjfjpx28Pii9fGisqwcsA7P07hwHTuamER0gvX7aBXXgWNbbOvgSMy5LulTf4y4cNTZYFJVdSUwAhijKMo7gKGu+IZUuOYXtGEhNPnsDSwFJirS0gl6cFKNmIJPVuM7ciiRK17Ekl9AyeZtAOg7xWBOTkE1lzsrXRt7VyXiGxHEnd89Q0leEbmpWQyac2OdMccTk/hzxY9cOr4/t618guJcE0fX7yZ53S60Bh23fvYYiS+vqh6250orP/uGiMhwftz4JXm5+Wxa/xvNmjfh0XkzasQtf+e/XHfTKFZ//wG5Ofms+yXRRRlf2FK/TMQrIoghPz+DOa+IwuNZdHnsxnqXy/nzCOYcE4PWzsOUnE7uzqNOyLZuGV9swhAZSM9fF1GeV0jJ8QxaP35zjZjI6/qjDw/kso9n0/2rJ4m8YYDdMnexadV6giKCWPjdCxTmFbI3cTehzcK4ac6EepdNGHM5AWGBPPL+4zz++dMMGD+o8RO2o/z3X1ACgzE+/jpqkQlLdjqGsTXvcGn+9St08VdgnP0SFTsSsaSnWsv6DsX7kRewFBVQkfSnS/I/V8FXv6ILD6HlV69SmV9IeWo6oTMn1ojJ+/Ab/MdcQYtPFlOZW0DRxu12y1wtZ+V69BHBtP/hRSryTJSlpNPk0Qk1YoLHDkQbFkTrD56g7RfPEHzdILKXf0vILcNou2ohHp4GTJtcP4wteeVmvCMCufbHpynLK8R0PIvYufWPQGg9rh/eYQFc8dHDDF85lzbXJTgh27odrarLNT8+jbmqLj0cqMuB936kzfUJDP/qccpyCznlBsML075IxCsykMt/eZby3EKKUjLp+Hj9x5dj7/7AJbcNJuGbJ/Hw1JO9ca8TsrVv9+pE/MKDuHftMxTnFZGTksXQ2TfWGXM0sfbhdk0ubUXWoTQqylx3nukSF/EcJkVVHUtOUZQ44FpVVWc6En/s0iHuW+u/6aO8MFen0GBeM7luXkdDO3boK1en0GBWdZ5bf9C/RJDFPSZdN4S3PV1/caKhvHGF629Q0FBOrXfbJ2L8bcUlOlen0GB2WXxdnUKDUVydQAMKcJPHKzSE7Z4Xzjcz7/iH/7rKFD11a6Of2xvnrHDL/4tDRx1FUYJVVd0MbK56P05V1c8aNTMhhBBCCCGEe3Dj2343NkdvK77yvPf3N3QiQgghhBBCCOFu6uxhUhSlPzAAaK4oytnbifsAtjekF0IIIYQQQlyY3HiOUWOrb0jecWAdMAZYX1VWAlxc91EUQgghhBBCXJTqbDCpqpoCpCiKskxV1fV1xQohhBBCCCEuUG78nKTG5uithr5XFOURoPqBAKqqzmuclIQQQgghhBDCPTjaYPoceAJw/VMthRBCCCGEEM7l4jlMiqJ4Ym2TNAN2A7eqdp6PpCjKw8DVQCHWRyKZ/+lnO3qXvGxgpaqq68++/ukHCyGEEEIIIYSDbgbSVFW9FAgEhpwfoChKK6Cjqqr9gLVA04b4YEd7mLYB6xRF+Qhraw1VVVc0RAJCCCGEEEIIN+f65zANBL6o+vkX4HLgh/NiBgGBiqJsADKBlxvigx3tYdoDvIW1saRwYT0EWwghhBBCCOFiiqJMUhRl2zmvSef8OhjIr/q5AAiy8ydCgWxVVROw9i71bYi8HO1h+gi4HegA7AXea4gPF0IIIYQQQvwLOGEOk6qqbwJv1vLr04B/1c/+Ve/PVwAcrPr5KNCkIfJytIfpXSAS61jAJkiDSQghhBBCCOE8PwNXVP08EPjVTsyfQGzVz62xNpr+MUd7mFqqqnpz1c/fK4qyqSE+XAghhBBCCOH+VNc/h+lDYLSiKLuBXUCyoijPq6o642yAqqpbFEU5rSjKH8B+VVW3NsQHO9pgSlUUZQ6wBegDpNa3wMv5wf8kL7dyeZnLV5AG84ixq6tTaDCrOs91dQoNZuSe+a5OocGM7Xa/q1NoMCfN+fUH/Uss+rGZq1NoMCcpc3UKDUavcXSgh/vLUPJcnYKwI1jn6eoUGkwQOlenIFxIVdUyYMR5xTPsxN3T0J/t6J76bkADjMU6NnByQycihBBCCCGEcFMWtfFfbsrRBtMKIA2YgnWS1buNlpEQQgghhBDCvUiDqV5hqqouU60WAOGNmZQQQgghhBBCuANH5zClKIoyE9gK9AJONV5KQgghhBBCCLfi+gfXuoyjPUwTgGKsc5iKgFsbKyEhhBBCCCGEcBcO9TBV3ZXi5UbORQghhBBCCOGO3HiOUWO7cO5nKoQQQgghhBANzNE5TEIIIYQQQoiLlCo9TEIIIYQQQgghzic9TEIIIYQQQoi6SQ+TEEIIIYQQQojzSQ+TEEIIIYQQom4WeQ6TEEIIIYQQQojzuGUPk9ag47al0wmMCubU/lQ+evDVvxV3w+J7CGsVhel0Pu/dvYS2fTsz/tlJ5KRlA/DJzDfIPprutPp4GHT0eHsanlHBmPalsmPqUrtxilZDj2XT+ePW5wHQ+Rvp8e6DeGg1ZP26i8P/t9JpOZ9PY9Ax7PX78YkK4vT+E/w07XWHYrReBq545V68An1J33aIzU9/jG+zUIa+MgVUlaT//sr+Tza4oEbW76XPWw/gHRVE/v4TbJ36mt04Rash7p1pJN62uLos5t4RRF5xGRVFZSROWIxaXumstP9n5RUVTJs9n1cXPenqVByiM+iY+fosQiJDOX7gGC9MW1JrrEar4ZE35/DUHfMAuKx/N+5bdD9ZaZkAvPKflzh59KRT8rZHb9Cz8K35hEeFcWR/Mo9NXVBr7BMvzqZFdHNyT+fyn4mP0jOhO48+P5P0tAwA5j/4LCnJJ5yVOlqDjuuXPoB/VDCZ+1P5/EHb7cRejL0yTz8jN705HQ+thsPrd7PuZefv07QGHVOWziAoKoS0/Sm89eBLDsfpvQzc/dI0fAL9OLztAJ89+z7+oQHcteR+PH28+G31Rn5671un1mXy0ocIjArm5P5U3n3Q/uMSa4u7YvI1dBnUg7LiUpbetYj48Zcz5K5rKMjOA+DlO56h1FTstPqcdSFt+//2uvy1HQRzYn8Kb9exjp0fp/cyMPmlafgE+nJk2wE+e/YDvPy8ufvl6Xj7Gdm3cTcrl3zs1LrcunQaAVHBpO9P5b8P2j8Xqy3u+sX3ENoqksLTBSy/ewmWSovdsguazGFyL91H9iU/4wzPD5uJl7+Rtv26OBx3SY8YPDQaXhw1F09fb2Kqlt38wY+8Mu4JXhn3hFMbSwBNx/Sl5FQOGwY9gi7ASGj/zjYxHp46En54mtCEv37XZHQcpoNpJF7zBEGxMXg1D3Vm2jXEjIqnMD2Hj4fOwdPfSLOETg7FxIyKI3P7Eb4YPY+gtk0JbB1F51sHk/ztH3w+ah6dbx2C1lPvghpBizHxlKTn8OPg2ej8jYTX8r0M/n4B4efU19g8FL+YJqwbOZ+MX3bhHRnkzLT/J6VlZYy/Yypb/tjh6lQcNmDU5ZxJP8O0K6fi4+9D136X2Y3TG/QsWfMCXft2rVH+3fvfMmvMTGaNmenSEyaAYWOuICs9ixsH346vvy+9+sfajbu0Z2c0Wg23j7gbo6+R3lVxX6xYxZ3XTuHOa6c4tbEEcOnIeAoycnh12Cw8/Y207me7ndiLsVt2bRyZh9J4a+yTNO/RlsCmzt+nxY1MIDfjDI8PewhvfyMd+13qcFyfkQkk7zjE02Pn0KRtMyKjmzB4wnA2fvYLC0bNot91gzB4ezqtLr1G9iM34wwLhv0Hb38j7Ws5VtqLC2kWRlTbZjw//jH2rttBYIR1P/b1/33Cc+Pm8ty4uS5pLMGFte3/2+vSZ2QCORlneHzYDIz+PrVuL/bi+ozsR/KOQzwz9lGiqraXhOsGs2XlBp4aNZvuw3rh7Wd0Wl26jexLfkYOS4Y9UnXOaLsvqy2uZY8YPDQevDzqMTx9vWjbr4vdMnHhcssGU5u4ThzauAeAI5v30qZPB4fjTKfz2fjuWgAqzRXVsV2G9WLaqgVMeG16I2dvK7hvR05vsOZ5elMSwfEdbWIspeWsHziT0vScGuVan6qDrwL+HVs0eq61aRrfgRMb9wKQtnkfTe18J/ZiKs0VaL0MgLUHqrLc+p3ofDxRPBQ0Bh0B0ZFOqkVNYfEdyaz6XrISkwiNt62TpbScHwfNouSc7yWsXyf0/kYGrJxLSK8YilKznZbz/8rTYGDlitcIDw1xdSoO6xzXhZ0brQ283Ym76Rxn/2BkLjPzwNCpnMk4XaO8z7A4nvtqCTNfn9XoudYnNr4bv2/YBsC2xO30iO9mNy4nO5eP3/ocgPLy8urygVcNYPm3b7Do7fmNn+x5WsV15EjVfvbo5n1cYmfbtxdT23IGoxcAiqIQ4YJ9Wvu4ziRt3A3A/s17ad/H9uJPbXEV5nL0ntb9mc6go6K8orpMo9UAoKrOuwLbLq4z+6tyPLh5LzG11MVeXLv4znj7+TDjkydpE9ue0yeyALj8tmHMWbOI8Y/f7pxK2HEhbfv/9rq0j+vEvo27ANi/eQ/t+tiev9QWV24ux1C9veipKK9gwyc/8ceaLeg99Xh4eFBhLrf79xpD67iO55wzJhFdS13sxRWezmfju98BUFF1bmmv7IJnURv/5abqHJKnKEqCqqobFEXxAsYBYcAeVVW/b8gkxsy/g8h2zavfWyoqKam6slVaWEJYdJTd5bwDfWziTh+3DlvpPDQWjV7LgQ27CG4eztrFn7L/1x1M/WIe0b07kPzbvoasQp30gT6UF1jzrDCV4ONgAyHti0RCB1xKj3emYzFXoHFRTwyAZ6APZVX/a7OphIBWtnWwF3No1WbGrn6c1lf15ERiEgUpWex57wcGPncXgxZPoiy/yGU9TPqgmt+Lr4PfiyHYl7IzJhInLGHg108Q0iuG078fbMxULwqTF9xDy/Ytq99XlldSbCoCoKSwmCbRTRz+WxkpGXy4+AP+/GUbC798jk69O7P3tz0NnXKtZj7zIG06RFe/ryivoLCgEIBCUxEtopvZXe7EsTQABgzrh06nY8u6rTRpEcVrC98m8ectvPPVUrr36cqfW3Y2fiWqeAf6UmYqAaCssJhQO9uJvRh7ZRtf/5rWCV244fVpVJSVozM0/rZ/y/y7aNrur4ZZZY3jSzGRtRxffAJ9beJ+W72ROV8+TexVcexL3E12aiY/LFvD418vYsSU0fz64Q+YS8oarS43zL/zvLpUVOdYUlhMeC11MZ5zrDwbV1ZYgimngKV3LWTml0/ROrYdKXuOcvJAKkd3HObpja/y8zvfcCat8S8IXUjb/r+9LjfPv5Nm56xjFedsLyWFJUQ4sL2cjft99SbmfPk0Pa7qw/7EPWSnWocWKh4ePLdpKX98uwVzqbnR6jJ6/h1EtvtrX1tZUVnda1paWGJ3XwbW7eX8uLPnlp2G9kCr13Jww67qh7ieWyYuXPXNYVoAJADfAqeA48BURVHGqKo66fxgRVEmAZMABgX1oItv9Pkhdn0xd1mN9ze9cB9evt4AePp6U5hjsrtcUY7JblzHwd3pd/sw3pm4CNWiUpxXyKFE604mNy0bn2A/h/JqKOYcEzo/a55aP2/MtdTHnl0PvoH5jInubz1A2emCxkqxXiU5JgxV/2u9nxelubZ1sBfTfcrV7H3/F/Z9vI4rXplCRPc25B3LYO3dL2EuKGbcN/MoOeOaepWd873ofB3/XspNJZiSrcM6C1Oz8IoIbLQcLyZvPFpzbsyDL87A29c6XMPb14gpx/H1pDDPxK5N1kZFZlom/iH+DZeoAxbOqjlPYf6rc/Hx8wHAx9dIXk5+rcsmXBHPDXeOY/qtM7FYLBTkFbB1o7V3Kv1EBoEhzl3finNMGHytvUKevt4U2dlO7MXovT3tLrdy5psU55i4/tUHKHLCtv/+3LdqvJ/0wgPVxw0vX29MtWz3ppwCm7ir7h3Nug9/YMMnPzP5pem07hZDwvWDWDHnTfZv2cvD/32Crd8kknPqtN2/+U/9d+7bNd7f8cL9NXKs7VhZeM6x8mxcSWEJmVVDvLJTMwmICOLgliQKc0yoFgu5GWfwDfZ3SoPpQtr2/+11+eC8deyuc7YX7zrWMdM569jZuOH3juLXD39g4yc/M/mlaUR3iyE7NQNTjomHKwHp7gAAIABJREFUE6Yw/7sl/LRsDdlVvZsN7cvzzi1vfGEKnudsB/b2ZWA9t7QX16Hq3HLZxOeqG0v2yi5kzuxBdzeODsnTq6p6k6qqc1RVHQHY7fdXVfVNVVV7qKraw9HGkj2HE/cSk2Dttm4T15EjW5IcjvMN9efySVfz9u0LKSsqBaD/nVdx2dVx1iEgbZuScdC5cwBOb0qqnrcU0rcjpxMd690K7t2eLgsn4qHX4tehBbl/Hm7MNOuUlphEs6r5VU3jOpK22bYO9mJ0Pl5UlFm73CvN5eiMBtpe24fYB0biGeSLzttA/vFM51XkHFkbk6rnLYX17UCWg99L7u5jBHZtBYBPy3AKUxpnZ3+x25W4i64J1vH+XeK6sGfLboeXvfaukfS7JgFFUWgR04LUgymNlaZD/tj4Z/V8pNi+3dmWuN1uXHBoELfcewMP3PwwxUXW3pmbJl/HFdcOQlEUotu1IvngMaflDZCcuJc2VfvZVnEdObbFdjuxF2OvrGXPdlz71EQ0ei0R7ZtzYofz92n7E/fQKcE6D6N9XGcObNnrcJyn0Yvyqv1Zhbkcg9GzqsyMarGAqqIz6JxTEeBA4h46VOXYLq4zB2upi7241L1HadmlNQBhLSM4nZrFuEdvo3VsO3QGPUFRIWQdc+5837MupG3/316X/Yl76Fi97nSqc3s5P+7stgFQbi7H0+jJ9XMn0LpbWyrKyqmsqHBKL/NZhxOTaFt1jtI6riPJdvZltcX5hvozYNII3rl9UfW5pb0yceGqr8HUTlGUNwB/RVFuVBTFS1GUx4Dcxkzqz9Wb8AsPYsbahRTnFXE4cS9BTUO5evbN9cbFjumPX1gAk1bM4r7PnqDnuAFsWv49PccN4IFVC9jzwx9kHnHuxMmTX2zCMzKI/r8spDy3kKKUTDo8flO9y2X9shMPTz1xqx/n8AsrqSxuvKEe9Tm4cjM+EYFc/8PTlOYVkp+SRfyjN9QZk7YpiT3Lf6TzLYMYu+pxtJ560jYlcfDLTYR3jWbEew+x8fH3XVQjSP0yEa+IIIb8/AzmvCIKj2fR5bEb610u588jmHNMDFo7D1NyOrk7jzoh24vP+lW/EhwRzIvfv4wp38SuTbsIaxbOhDl31LvsmuXfMGjcEJ77ajG/fb+FE4ede5HkfGu//JHQiFD++/N7VT1GfxLVLJIHHru3RtyI8VcSEhbMKx8v5u3Vr3LN9cP5ZNmXXHP9cN779g1+XbuBY4eOOzX3XasT8Q0P5L61z1KSV0hOSiZXzr6xzpjkxL12yw6t24XWoOOuTx9n3SurMLtgn7Zl9QYCwoOYt3YJRXmF7EvcTUjTMK6bfWu9cT+/v5bLbx7KnC+fRu+pZ1/iHta8tpLrH53A3FXPkrrvOJlObGRsXb2RgPAg5q59nqK8Qg4k7iG4aRhjZt9Sb9zR7YcozDUxa/UzZCSf4viuI6x99UtGz7yJ/3w+nzUvfU5xQZHT6nKuC2nb/7fX5bfVGwgMD+bJtYurtoM9hDQNY/x524u9uF/e/47Lbx7K7C+f+mt7WbqSsY/czGNfLWTXL9s5dSTNaXXZvnoT/uFBPLh2IcV5hdXnliNm31RvXI8xCfiFBXDXikeY8tnjxI4bYLfsgncRz2FS6upeUxQl5py3p4FyYDzwoaqqJXX94QdbXu++tf6bLi9VXJ1Cg0nRaVydQoMJr7hgVjFG7nH+ZP7GMrbb/a5OocGcLK996Ny/zZUG+/Om/o1O4rqLRw1N7573XvqfZFjqPC0QLhLs4bw7Nza2IJzXg9vYnj/+33/dyWXBXVc0+omX31s/uOX/pc45TKqqHgRQFCVYVdUzVcVvK4oyDvissZMTQgghhBBCuAE37gFqbI5e2jr/6YIXziVkIYQQQgghhKhFfbcV7w8MAJpXzV0C8AFyal1ICCGEEEIIcUG5GO4EWJv6bit+HFgHjAHWV5WVADsaLyUhhBBCCCGEW5EGk32qqqYAKYqiLFNVdX1dsUIIIYQQQghxoamvh+ms7xVFeQSovmG+qqrzGiclIYQQQgghhFuxuDoB13G0wfQ58AQgT+gUQgghhBBCXDQcbTBlAytVVa1ozGSEEEIIIYQQ7kdu+lC/bcA6RVE+AgoBVFVd0WhZCSGEEEIIIYQbcLTBtKfqpQJu+QReIYQQQgghRCO5iHuYHH1w7UeAAegG6IAPGy0jIYQQQgghhHATjjaY3gUigbVAE+C9xkpICCGEEEII4WYsTni5KUeH5LVUVfXmqp+/VxRlU2MlJIQQQgghhBDuwtEGU6qiKHOALUAfILW+BZ56vtM/ycutPDNjv6tTaDDv5O9wdQoNZoX+wlnHxna739UpNJjPt7/k6hQaTPmKZ1ydQoMpWnvI1Sk0GJ8Hx7g6hQajhLd0dQoNRk3Z5+oUGo7G0dOjfwFvH1dn0GBemCjX613pYr5LnqND8u4GNMBYoACY3GgZCSGEEEIIIYSbcLTBtAJIA6YA/ljnNAkhhBBCCCEuBhfxHCZHG0xhqqouU60WAOGNmZQQQgghhBBCuANHB+mmKIoyE9gK9AJONV5KQgghhBBCCHcic5jqNwEoxjqHqQi4tbESEkIIIYQQQgh34VAPk6qqZcDLjZyLEEIIIYQQwh258RyjxuZoD5MQQgghhBBCXHQuoAcNCCGEEEIIIRqDKj1MQgghhBBCCCHOJz1MQgghhBBCiLpdxD1M0mASQgghhBBC1EmG5AkhhBBCCCGEsCE9TEIIIYQQQoi6XcQ9TG7bYCorr2TGf9eTmV9Mm4gAFoyNR1GUGjF/HM3g1Z92ApCeV8SUwV25pls0725IYsOBNLz1Wl64eQA6rcYVVUBr0HHd0gfwjwoiY/8JvnzwNYdjPLQabnh9Gh/eubg6tu/kEcQMugxzcRkf3bWYyvJKp9XlXAaDnjeX/x9RTSLZn3SQ+++eZTdOo9Gw9O3nCI8MI/nwMR6aOpcBg/ry/ItPcuLEKQBmTJ1L8pHjTsz+Lx4GHZ3fmY4hKoTCfSnsu+9Vu3EdXroX79ZRmE/ns+f2xaiVFrtl7kBn0DHz9VmERIZy/MAxXpi2pNZYjVbDI2/O4ak75gFwWf9u3LfofrLSMgF45T8vcfLoSafk/XeVV1QwbfZ8Xl30pKtTqZtGi+Hqe1B8A7Fkp2H+bpndMG2PoWhaXQrlpZStfhUslegSxuERFY2am4n5+3ednLgdOj1+jz2JJjSMimNHMS18qtZQr9Hj0PfqTf7Mh6rLjPdORfH0pHDJc87I1mFl5RXMePc7MvMKaRMVzIKbBtsca0rKypn1/g/kFpXS9ZJIpl8T56JsbZWZy3nohffJPJNHm+aRPHXv9Tb5F5eamfXKh+SZiujatiXTbxpBdm4Bjy79mKLSMobFX8ZNV/Z1UQ3+UlZewYwPfyUzr4g2kUEsGN/P9rifnM6rP2wHID2vkClXdCMywMem7JrubZye/7nKyiuY8f5Pf9Xl+gF26nKKV7/bBkB6biFTruzBgA4tmL78RyoqLcTHNGXSkG6uSL+GsvIKZrz9DZm5JtpEhbDgtivtbyPvfktuUQldW0UxfVQCpeYKHn7nG04XFNOuWSiP3TjERTUAjUHH6NfuxzcyiOwDJ/h6+ut/KyZ24pVED+zKxzc9i9agY+TSqfiEBpCRdJzvZtnfr4sLi9sOyVuz8yjhft58OnUEBSVmthxJt4mJbRXBe5Ou5L1JV9ImPJB2UUGk5ZhIzsrj3UlDiW8bRWZBsQuyt+oyMp6CjByWDpuNl7+R6H6dHYrRGnTc/fUCWvXrVB0X2CyU0LZNeGf8fA6t24VfRJAzq1LD6PFXk34qkyH9RuMf4EfC5fZPHq68ahD79h5k5JU3ExYRSsdO7QBY8e4njBp2C6OG3eKyxhJAxNh+lJ7KYevAh9EFGAnq38Umxr9nDIpWw7bhj6L18SJoQBe7Ze5iwKjLOZN+hmlXTsXH34eu/S6zG6c36Fmy5gW69u1ao/y7979l1piZzBoz020bS6VlZYy/Yypb/tjh6lTqpWnfG4spl9L356F4GvFo0cEmRvEPwSM4irJPF1F5fC+KbyAezWKgooyyj59FLTgNBi8XZF+T5+AhWE5nk3v3RBQfH3Tde9iN8wgLx/OKK2uUaWPaoY/t5Yw0/7Y12w4RHuDDpw9fT0FxGVsOnrCN+fMQnVtEsPyBMSRn5HA0I8cFmdq3ZtN2woP8+WzhgxQUlbBlzyGbmG8Tt9OldQuWP3kfyWmZHD2ZyX+/T2TkgFg+mD+VVb9upbi0zAXZ17Rmx1HC/Y18Om0kBSVlbDl8yiYmNjqS9+65ivfuuYo2EUG0iwq2W+Zqa7YfsdblwTHWuhyy3Z/GRkfx3pRreG/KNbSJtOb97Y4jRIcHsvy+a9h5PJOTOQUuyL6mNVv3W7eR2bdY63Ig1Tbmj/10viSS5Q9dT3J6DkczzvDzzsO0aRLKRzNv5LcDqSSnn3FB9ladRsVTkJ7DsmFz8PQ3csk551f1xfg1Cabz2H7VcTHDe5J14ATvXfMYl/TrRHCbKKfVw9VUS+O/3FWdDSZFUQIURemmKIqnoig3KYrysKIoYxVF0TV2YluPZtC7dSQAPVtFsO1oRq2xJeYKTuSYaBsRyO/JGZhKzNzx1vdsT8miSaBPY6daq1ZxHUneuAeAY5uTuKSP7cmSvZiKsnKWDptFQfpfB+VW8Z3w8jNyxydzaREbQ+6JbOdUwo74hF5s+HULAIkbthLXr6fduF9/3sQbS5ej0Wjw9/PFZCoEYPjVQ/jmp495c/kLTsvZnsC+HclZvxuAnI1JBPbtaBNjzs7nxFvfAmApr6i1zF10juvCzo3WhsTuxN10jrPfmDOXmXlg6FTOZJyuUd5nWBzPfbWEma/b7zV0B54GAytXvEZ4aIirU6mXpnk7LKn7AKhMPYCmWYxNjEfz9uDpjWH8f/Bo0gY1/zSa5u1RAiMw3DALDN5QVuLs1G3ounbD/Kf1anj5zh3oL7XfGPe5dypFy978q0CjwXjn3RS/+44z0vzbth5Oo3dMMwB6tmnKtsO2J7Z6rYbS8gpUVcVcXuGyUQv2bE06Qp/O1t6Unh2j+SMp2SZGr9VSYjajqiplVfnrtRpKzOWUV5wdqaDYLOdsW5PT6d3aevLZMzqSbUdtL5SeVWKu4MSZAtpGBtVZ5ipbj5yid5umAPSMjmJbsm3j76zqvKsaesVl1u9KReXgSdc1Ms7aeugEvdu1AKBn22ZsO2R7UUGv1VBqPmcb0Wi4JCKIET3bA6DTuHabaRHXgeOb9gJwfPM+mts5H6stZsjjt7B+0afVcWcOn2Tvl5sAqDS7ZqSPcL76epi+BtoDa4CrAD9gLLCqkfMiv7gMH089AEaDjvwSc62xvx1Jp2erCAByi0oJNBpYdtdQsvKL2ZGS1dip1so70IdSk7WHq7SwBK8A4/8UA2AM8qUox8Sy6+bjHxlEi1jbE6/G8vTzc1m59v3qV3h4KKYCEwAmUyGBgf52lysuKqa0pJRV331AdvYZUlPSSDmWynNPv8yIwdcTHhFCn/hYp9XjfLpAXypM1hPRysISdAG2jeuSYxkU7EgmdFgsHjotZ37dZbfMVSYvuIdnvlhY/QoKC6LYVGTNvbAYHzt1qk1GSgYfLv6A/1zzIEFhQXTqbdsjKv4exdMHtaqxo5pLwNN2+1a8fKGkkLJPn0PxCcSjSRsUL18sp09S9vGzaFpfhuLr+hNADz8/1CLruqUWF6H4+tnEGC4fTMXRZCpSUqrLvMffQOlP32PJy3Varn9HflHpX8caTz35xaU2McO6tyFxfwqjnvmIluGBNAuxv89zhbzCYny8PQHw8fIkv8h2VMWw+MtI3HmQkTOe45KoUJqFh3DT8H4sW/0LIx9axFV9u+Fd9T9wpfzic74Lg5784tp7vX47fJKeraPqLXOVGnXxrKcuh9Kq8x7erTWmUjMPrfjJ2gipcP0JeX5RKT5e59SlyM42EtuOxH3HGDV/OS0jgmgWGkCH5uFcEhHEB79sp33zMKIjXdfz5xXgQ1nViCNzYQledo6N9mI6XNuHrP2pnD7nQkrG3uPkJKcTe8dQMvYe44ydntAL1cXcw1TfHKZKVVU/VBTldlVVbzxbqCjK7/aCFUWZBEwCeHnSNUwc8r+fDAcYDRSWWhtJhWXlBHgbao3dcCCNgR2bA+Bj0NGi6mDWJMiHLBcOySvKMeHp6w2Ap683xTmm/ykGrI2pM1VX23JSs/CNCGykrG3NnjG/xvuX31yIr58vAH5+PuScsX8iFBjoT1FRMdcOvYlPv1pGXN+e7E86yMZ11t6pE6mnCAl13YlgeY4Jra91qJPW1xtzLUMfQoZ2p9ldw9h1yyKwqLWWucIbj9acF/fgizPw9rWelHv7GjH9jeEchXkmdm2yzgnMTMvE341OCv+t1JJClKrhdIrBC0oKbYPMJVhyrD3oav5pFJ8AVHMJam4mqCqqKddaZnLtMDBLfj6K0bpuKUYjloJ8mxh97z5owsLQ94hF07Q5nteOQtejJzrVgoePD0pAEPp+/TFvXO/s9GsV4OP517GmtIwAH9vhj8t+3M7YuE6M7tOBR5b/wM5j6XS9JNLZqdoV6GuksKqRZyouJdDXtlH+zupfGDe4N6MH9mLmSx+y89BxvvxlK49OHE1sx9bcNf8Nhva5lMgQ5x1X7AkwnvtdmAkwetYau2H/CQZ2alFvmav8vbqkMrBTy+r3j49LIMjHixnv/0SQ0fXDcQOMXhRWXbQuLDHb30a+/4OxfbswOr4zjyz7lp1HT9G1VRSfbtjFjuSTPHvHcGenXUNJrgmDn/Vcy+DrRXGu7bmWvZjWgy7DLyqYSxK6EBQdSffbhvDn8h+57OZBNO0Zw+pa5j6LC099PUxvKYqyBrAoivKDoiivKIqSCHxlL1hV1TdVVe2hqmqPf9JYAujVKrJ63tLW5Axiq3qQ7HwmfxzLqO5hat8kmKST1mFGJ86YaBLo+4/y+CeOJiYRnWC9St8qrgPHtuz7n2IA0vceI6pLKwCCW4aTm+q6nrNN63+j/0DrvKX4hF5s3rjVbtzk+yYwYuRQLBYLJSWleHoZmDTlNq4dPRxFUYhp35oD+484M/UacjbuIXjApYB1eF5uou3/Xh/qT4t7r2bnzQuprLqqZq/MXexK3EXXBOtQqS5xXdizZbfDy15710j6XZOAoii0iGlB6sGU+hcSdapM3V89b0nTrB2VJw7axFgyU/CIaAmAEhCKmp+NJTPVWqYoKH5BWApcPyynfMd29N2t+3Vd126U77SdQ2Z6Zj5506dS8NQ8Kg4fpHT1SvIfup/8GdMoXPoK5q1b3KqxBNCrTdPqORlbD58ktnUTm5iiMjMGnXVIkU6robis3Kk51qVnx9Zsrpq39EfSEWI7RNvEFJeWoddZr4/qdRqKS8uqynRoPDxQFOvEflfrFR3Jlqor+VuT04ltZb9RqqoqfxzNoGd0ZJ1lrtSrdRO2HEoDrMPzYqPt93ypqsofR05V9zBtP5rBU19swlxRyaFTZ+jSIsxpOdemV0wztuy3Hg+2HjpBbNumNjFFpWYMVevY2W3kYFo2G5OOsWjiVS4fknc8MYlLquaRt4jrSOpm2+O9vZiv7l/KB2Pns3rqK2TsOcafy38krH1zWg/syqp7X8biBj2AznQx9zDV2WBSVfVDYDywCHgXWAuMV1W19tsjNZDhXS8hq6CYcS99jb+3nmZBPixZ+6dN3N60M0SHBVQfzC5tHkqgtyc3Lv2WliH+dG7munkOu1cn4hcexL1rn6E4r4iclCyGzr6xzpijiUl2/9aJ7UcozjUxefU8Tienc3LXUWdUwa6Vn31DRGQYP276krzcfDau/41mzZswd96MGnHvvf1frr9pNF99/yG5OXms+zmRd9/6iPE3jeSbnz7mu29+5vBB2/H2zpLxxSYMkYH0/HUR5XmFlBzPoPXjN9eIibyuP/rwQC77eDbdv3qSyBsG2C1zF+tX/UpwRDAvfv8ypnwTuzbtIqxZOBPm3FHvsmuWf8OgcUN47qvF/Pb9Fk4cth2nLv6eygO/4+ETiOctj6OWFqHmZaNLGFsjxpJ+FLWkEMONc1BzM7FkHKfy8J8ovsF43jiHyn2/QZFtb46zlf7yIx4hIQS+sQzVZKIy/STGSfe4Oq1/bHiPGLLyixi38GP8vQ00C/FnyerEGjHX9e3Mp4l7ufX/PqesvIJedk4YXeWqvt3Iyilg7MOL8fPxpml4CIs/+LpGzHVXxPHZT1u45bGXKTVX0KtTG+645nKe/+Brbnz0JWJaRNEyMtRFNfjL8Muircf9F1ZZv4tgX5assb0gtzftNNHhAdUn6LWVudLwbq3JKihi3OIvqtYrX5Z8/ZtN3N4T2URHBFbnHd+uGWUVldy+9GvuGnQZ3oZGnzJer+Gx7cjKL2TcU+9X1SWAJV9uqBFzXf9L+XTjLm59/mPrNhLTjM837ubUmQIm/t9nTFj8CZuSjrmoBpC0ajO+EYFM/O5pSvMKyU3NYuCcG+qMOV7L+dhlNw3Ev2kIN336KDd/PpdWbnTjJ9F4FFWtfziRoijBqqqeOef9OFVVP6trmZLPF7hunFIDe2bGflen0GDeyd/p6hQazAq97V1u/q1eMhS5OoUG8/n2l1ydQoMpX/GMq1NoMEVrbe+e9m/l8+AYV6fQYJTwlq5OocGoKfZHSPwradyj4dUgvF1386uG9sLETa5OocHMSvnA9Xda+ZsyBwxo9HP78HXr3PL/4uhtxVee9/7+hk5ECCGEEEIIIdxNnZdQFEXpDwwAmiuK8lhVsQ/gPg+hEEIIIYQQQjQqd55j1Njq63M+DqwDxgBnZ+qWAO7/xEghhBBCCCGE+IfqbDCpqpoCpCiKskxVVfe6tZEQQgghhBDCKVSLa6cXKYriCXwONAN2A7eqtdyMQVGU6cBVqqoObojPdnRW4/eKojwCVD/ZTlXVeQ2RgBBCCCGEEELU42YgTVXVEYqifAMMAX44P0hRlBbABCC7oT7Y0Zs+fA4kYx2Wd/YlhBBCCCGEuAi4wXOYBgI/Vv38C3B5LXEvArMaptZWjvYwZQMrVVV1/ZPthBBCCCGEEBebYODsgwkLgJjzAxRFuRHYBTTocw4cbTBtA9YpivIRUAigquqKhkxECCGEEEII4Z5UtfHnMCmKMgmYdE7Rm6qqvln182nAv+pn/6r35xsBNAeGAjGKotynquor/zQvRxtMe6peKuCWD5QSQgghhBBC/HtVNY7erOXXPwNXAF9gHZ73f3aWvxFAUZSWwNsN0VgCx+cwfQQYgG6ADviwIT5cCCGEEEII4f7cYA7Th0ATRVF2Y30mbLKiKM83dr3B8R6md4HDwFqgN/Ae1jtVCCGEEEIIIS5wrr6tuKqqZViH3J1rRi2xx4EGuaU4ON5gaqmq6tkG0veKomxqqASEEEIIIYQQwl052mBKVRRlDrAF6AOkNl5KQgghhBBCCHdi/xGxFwdHG0x3A9OAscB+YHJ9C2TMv3Ae1eSthrs6hQYz1+cyV6fQYN72KHR1Cg3mpDm//qB/ifIVz7g6hQaju7VBH+PgUj88/5irU2gw/eaudnUKDcavTf2D9v8tDCMHuDqFhmMpcXUGDWb/lI2uTqHBlHn4uDoFcZFy9KYPK4A0YArW2/i922gZCSGEEEIIIdyKalEa/eWuHG0whamquky1WgBcOF0uQgghhBBCCFELR4fkpSiKMhPYCvQCTjVeSkIIIYQQQgh34s49QI3N0R6mCUAx1jlMRcCtjZWQEEIIIYQQQrgLh3qYqu57/nIj5yKEEEIIIYRwQxfzXfIc7WESQgghhBBCiIuOo3OYhBBCCCGEEBcpmcMkhBBCCCGEEMKG9DAJIYQQQggh6qSq0sMkhBBCCCGEEOI80sMkhBBCCCGEqJNqcXUGriM9TEIIIYQQQghRC+lhEkIIIYQQQtTJchHPYXLbBpOi1xG2+DE04aGUHz5G9pyFtkEaD8KenYMmNJjylBOcfnwxnj26EHjf7QBoI8PIfeU9Cr/+0cnZV6Vn0DHqtfvxiwwi68AJvpn++t+KiZ14JdEDu/LxTc9iDAtgxJLJ6I1e7Fu9mT/f+8GZVUFj0HHF6/djjAoiZ/8Jfplmvy7nx+j9vbnyrel4aDWkrt/N9hdXofUyMOiVe/EK9CVj2yF+e/pjp9blXDqDjmmvPUxwZAipB1JYOv2FWmM1Wg3T33iE5yc+VV12z+L7iWzVhIIz+SyZ/CyWStf2V+sNeha+NZ/wqDCO7E/msakLao194sXZtIhuTu7pXP4z8VF6JnTn0ednkp6WAcD8B58lJfmEs1L/i0aL4ep7UHwDsWSnYf5umd0wbY+haFpdCuWllK1+FSyV6BLG4REVjZqbifn7d52c+P+mvKKCabPn8+qiJ12dSp08DDoS3rwf76gg8vafYPP9tvsAAEWrIeGdB1h/25Ia5e3uupKowV355bpnnZFu3fQ6Qhc+YT2+HDnKmcfs5KTxIOSpR6uPLznznkfx9CT4qdloAvwp25VE3ktvOj/38+l0GKc9iUdwGJWpyRQvfabWUMOwsWgv603R0zPwCI3Ae+pcUFXMv67BvG6tE5O2r6y8khmfbCQzv5g2EQEsGN0HRal5gvbHsUxe/Xk3AOl5RUwZ1IVgH0+eXP07UQE+ADwxshctQ/ycnv+5ysormfFpIpkFxbQJ92fBqN726/LLHgDS84uYMrAL13S9BID3txxk06FTvHHb5U7P/XyKQUer12eiiwqhZP9xUqbZP062WPIAntFNKD+Tz9G7nkHj40Wrt2ahaLUUrN9OxoufOjnzv2gNOsa/9gB+kUFkHjjByumvORwzcvFkQlpFUXQmn08mv4Cl0mK3TFzY3HZInvGqwVRknubU+Lvx8PPBq093mxjvy+MxH0omfcI0NCFB6GOiKd22m/QJ00le5rrcAAAgAElEQVSfMB3zoWOUHTjiguytOo6Kx5Sew7Jhc/D0N9KyXyeHY/yaBNN5bL/quO63XcHuTzfw/qgnuPS6/ui8DU6rB0CbUfEUpufw+dA5GPyNNE2wrYu9mDYj48g5lMaq0fOI6NEW32ahtBkVR9b2I6waPY/Atk0JaB3l1Lqcq++o/uSkn+GRYdMx+hvp3K+r3TidQc/T3yymc99Lq8tierTHQ6vhsVEz8fLxpkvCZc5Ku1bDxlxBVnoWNw6+HV9/X3r1j7Ubd2nPzmi0Gm4fcTdGXyO9q+K+WLGKO6+dwp3XTnFNYwnQtO+NxZRL6fvzUDyNeLToYBOj+IfgERxF2aeLqDy+F8U3EI9mMVBRRtnHz6IWnAaDlwuy/3tKy8oYf8dUtvyxw9Wp1OuSMfEUp+fw7ZA56P2NRPS33QdoPHUM+24+keft64xNgmk1vp9NvKsYhw2hIiubjBsn4eHri2cv2+OL14C+mA8nkznxATTBwejaRuM9bBDmPfvJnPgAulYt0LZs7oLsa9L3HYIlJxvTI3eiGH3Rdu5hN04JCUff/8q/lhtyLeVbN1D4xFT0Q0aC3rnHFHvW7D5GuJ83n04ZTkGJmS3JGTYxsZeE896dQ3jvziG0CQ+gXWQgAONi21SXu7qxBLBm93HC/bz49J4rKSgpr70uEwfz3sTB1rpEWOtyKq+Ir3cec3bKtQoaNQBz+hkODJ2G1t8H3wTb46Qxtj2KVsPBax9G4+OFX8JlBI7sT+mhExwa/QjGHu3RNwtzQfZWXUbFU5Cew+vDZuPlbyS6X2eHYpr3aIuHVsPbox7H4ONFdIL9souFqiqN/nJXdTaYFEWZqyjKCEVRPJ2V0FlePbtSsuVP+H/27js8qip94Pj3Tk9m0kkhoSNdeg/SpAjqKirYsIJdwcaKgoqKFZV1EdGfrghY1lUUcO2uBSHgAtJ7S0JLAiFtJpPp9/fHxECcSXE3mZmF9/M885g5eQffk3vPvffcc84doGLdZkx9uwfEVGStp/TdT0CrQRNjwWcrr/qdYjKia5GOe1/4DjotMzuTs3o7ALlrdtJyYOCFX00xI2ddz09zTt2N8brc6KMMaHRaf4HayMn/TsagzhxZ5c/z6JqdpAepS00xerN/91EUSOrSEq/Lgy7Kf3LWGvX43J5QVCGoLpnd2LZ6CwA71myjy8DAi0AAt9PF9DH3UZR/sqqstLCErxd+DoAnjHU4Xd9Bvfj3zxsA2JC1kT6DegWNKzpRzIdvLQXA7XZXlZ9/0TAWf/l/zPnb7MZPtgbaFh3xHdoJgPfQbrTNOwTEaFp0AlM0xiv/jCajHWppIdoWnVAS0jBe8wgYo8FZEerU/zCT0ciyJa+Tmtwk3KnUKW1QZ/J+9rfv/KydpGUGHgO8DjdfjpyBPa+4Wnnv2dez+bnw3V3+PVPfHjj+7T+/ODZswtgn8ALQsWYd1veWnjq/lNvB5UYx+Y9disEAp7WdcNF16YVnm78unh2b0HUJftMn+oZ7qPjwrWpliikaFAXFYECb3rzRc63LuoMFDGibBkC/1qlsyC6oMbbC5eFwkZX2lZ2M73ceZuL/fc2DH65CVUN8ggxiXfbpdUlhQ87xGmP9dbHRPi0egDlfbWTKiG4hybM+YgZ1xbpqMwDWNVuJGRjYQfCcKOH4wn8CoJ52PtSY/TeuFEUhqkubEGQbXOvMLhxY7R/Ny16zg1ZBrmGCxdgKS/n3wq8B8Lq9AEHLxJmvril51wOpwEuKouwHPgc+V1X1SGMnpomPreoA+Wx29K2aBcSoFQ4Amr43D++JIjxHT93BiRrQC8e/w3vXNiregqPMDoDTVkFim6b1iul86UCO7zrEyX1Hq+I2LPyamz5/moF3X8Km93/AXeEMTSUqmRIsuKz+PF3WCuKD1CVYzObXP6f50G6MfvNef0fJpGf/8jWMWzGLNhf142jWDspyaz6RNLRJT99O844tq957PV7sZf79rMJmp2mbjHr/W/k5eQD0uaA/Or2OLStDv79Nf+4B2nVuW/Xe4/ZgK7MBYLOW07Jt8Iugw9n+Jjxs7GD0ej1rf1pHRst0Xn/hb2R9v5a3P1tA74E9+HXt5savxO8oJgtqZWdHdVWgJKYGxkTFQIUN54rXMF79MJqMdihRMfgKj+L58i1Mk5/D8+t3qNaiUKd/xjIkWHBXtm+3rYLYtoHHgGBaXTaQ4p2HKN17tO7gENHEVT+/6FoGtpPfzi+pi+bjLSzCezSP8vzjpL7zKtEjh+JYtwnP0byQ5h2MYolFtfvbvFpRjqZpYF30mSPwHjqA70hOVZnz2+VE3/Znou+Yjmqzgj78I0yldicWkwEAs0lPzklrjbG/HMinXxt/h6RZooW7zu/GkA4Z3PDWt2zIOU7f1oHHjVAqrXBhMeoBMBvrqMvBfPpV5vvl1hzap8XTJjn8o2S/0SXE4q1s+15rBcYg50ln5fkwbswAFL2OspWb/CNNQ3vS5s2H8bncaCq3bThExVtwnnatlVTD9djvY4py/J32jhf0QavXsn/lVlSfGlB2tlB9kTsC1Njq6jAVqKp6D4CiKN2APwFLFUUxqKoacOtaUZTbgNsAnsnoyDVJgZ2c+vIVl6KxmAHQxJjxFZcFxGjiYvDZHeTdcB9pb72IqW93HOv9owXRQwdS/v3q//j/3xAqiq2YYqMBMMVEUVEceMAMFtN2RE9i05NoPaQbSW2b0uvGUaSd24qvZyzk0NqdXPP3Gez+5y+UHTsZ8O81FkeRFUOMP09DbBSOIHWpKeanaW/hKLIy6vUpVBSW0fPuP7Hz3R/Y/eFPjJh/N6m921Hw676Q1GPho/9X7f3df72f6Fj/fhYdY8YaZD+rTe+RfRl78594cfLTqL7Qz2F+4ZHq60Rmv/YYllj/PH5LjJmSotIaPztk9CCuuWUC998wHZ/PR1lJGetW+Uen8g7nk9AkofESr4VaYUOpnE6nGKOgwhYY5KrAV+S/QaKWFqJY4lFdFajFBaCqqNZif5l0mBqMs8iKvrJ962OicBbVfAF4uoyRPYnOSCJ9aDdi2jal/c2j2PtOeNaV/sZXctr5xWLGVxLYTjRxsfjsFRRMmkrK6y9h7N0DY8+u2D75nPIVX5L0zEwM3Trj2roz1OlXo1pLUaL9bV6JNqNaA+ui7zUQTVIKum590TRtjmH0ONxrf8T+yixUezmWp19HLSsO+FyoxZuN2BwuAGwON/G1TD3/ec9Rzu/sv8aIizIyoK1/e6bHmykqdzR+snWIjzZic/pHIG3OuupyjPM7+evy895j5JfaWbs/n5yTZXz4771c3b99SHKuiaeoDG1l29fGRuMNcv4HiBvVj5RJF3Pg5meg8nx4aNqreIrKaP36Q3gKaz4fNTZ7sRVj5bWWMSYae5A61BTTYWQvBtx8AR9MfrmqsxSs7GwQAYO3YVPvNUyqqm5VVfUZVVUHAGNqiHlTVdU+qqr2+W86SwAV6zYRlemfVx7VtwcV6wPvdMfdMAHz6CHg86E6HCjGUwckU9/uONaFd4QpN2sHrSrnybbI7ELumsATa7CYf05dwPvjZ/PZlPnkb8tm4+LvMJhNeJ1uf8NU/VPZQulI1g6aV87TzcjswtEgdQkW07R/R4Y8dzMag46kzi0o2LgfvSUKb+WJxOdyozeH787mjqytVeuWumR2ZeeabfX+bFxyPBfffhlzbp6NIwJO0ADrV/1atR6p73m92ZC1MWhcUnIi1991Dfde9xD2cv9ozsTbr2L0pSNQFIW2HdtwYE94prN6D+2qWrekbd4R7+E9ATG+glw0aa0AUOKTUUtP4Cs45C9TFJTYRHxlobuhcDbIX72DpkP97TttUBcKghwDgsm6ewHfjZvN6jvnU7QtO+ydJQDH+k2YBvjX+pj69sCxIfD8EjNxAtEjh1aeX5woJgOa6ChUl/+CXnW50USFf52cZ8fGqnVLui698OwMPO/Z5z+N7cmp2Oc9hTd7L65vl6PPHIHx8htQYuJQTFH4Co6FOvUA/dukVa31WXewoMZRIlVVWZ9dQL/W/hGmd9fs5uttufh8KgeOl3JOSnzIcq5J/9app+qSXUDfVsHX76iqyvqcgqoRpufHZ7Jo8kieHz+Qzk0Tw95ZArBmbalatxST2Q1rkPOkLjme1DvGceDG2fgqzymW/l1o/tydKAYdUZ1bUb4x8FgeKtlZO6rWLbXO7ExOkONXsBhLchyZt1/E+ze/hKvyPB+sTJz56uowTQ5WqKpqo8+hsn3xA7qUJmR8/H/4yqx4juSR+MBt1WLK/rGCmHEX0HTJX/GVllGxxn933HBuB1wHclFd4Z1fvmP5GmLSEpj09bM4SmyUHDrO8JnX1BqTm7Uj6L+1dsE/Of+xidyw4kkKduZSnB24gLQx7Vu2BnNaAhO+fRZniY2y3OMMePSaWmOOrt7B4R+3oDUaGPfJY2yctwKP3cmOxd/R5foRjFs+C63JwNHVwescCquXryQxLZEXvn4FW4mN7VlbSW6ewsSZN9X52SFXDCc+JYGH353FrKXPMuzKEY2fcB2++vQ7ktOS+fv3iypHjH4lvXlT7n38rmpxF185hiYpScz/8GX+tuI1Lrn6Qv6x8FMuufpCFn35f/z41c9k780JSx28u/+NxpKA6fpZqI5y1JIT6IeMrxbjyzuIWmHDeO1M1OICfPk5ePf9ihKThOnamXh3/gLl4bubeSbK+XQN0WkJXPivZ3GV2LDmHKfn49fU/cEIVP7V9+iSm5D297cqzy/HiL/39moxto9XYLlkDKkLX8VXWoZj7QasH68gZvyfSF34KorRgCMCHtbhWv0vNIlNiHnhb6i2MnwFxzBNvKPOz7lXf4uubUfMDz1HxeJXQ5Bp3S7s1orjZXYmvPYlcdEGmidamPt14E2f7UdP0jYlDqPev6b36v7tWbHpINe9+Q3DOzWjbUpcqFMPcGG3lhwvq2DCgq+Ii6qsyzeB+8v2o0W0TT5Vl0hUtGwlhrQkOn37VzwlVpy5eWQ8elO1mKTx56NLSeSc956g/SfPkXTVCEp//BWN0UD7T54jf97H+Ozh61xsXZ5FbFoid379HBUl5RQdOs7omdfWGnMwawfdrxhMTEo81787nUlLH6fnlUODlp0tVJ/S6K9IpdRncaSiKEmqqp487f0EVVU/ru0z2d1HnTEDd/8oCe9c6IYUfwY9+XKlJshUrf9R+1xnzmjIz3/uGO4UGoz+hkfCnUKD+ajb4+FOocEMTg//2qGGEtvuzDkoG8cNC3cKDScMU6wby66HQr8WtbF8prGEO4UG80Tu+5HbO6jBzrYXNfq1fecDX0Tk36W+U/KW/e791IZORAghhBBCCBGZfKrS6K9IVetDHxRFGQoMA1ooivLb7UkLICuphRBCCCGEEGe8up6SlwP8BFwBrKwsqwDCP3FbCCGEEEIIERKR/MWyja3WDpOqqrlArqIoC1VVXVlbrBBCCCGEEEKcaeoaYfrNN4qiPAxUfeuYqqpPNU5KQgghhBBCiEhyNn8PU307TEuBJ4BGf5y4EEIIIYQQQkSK+naYTgDLVFX1NGYyQgghhBBCiMgTyU+xa2z17TBtAH5SFOUDwAagquqSRstKCCGEEEIIISJAfTtM2ypfKnD2di+FEEIIIYQ4C53NT8mr7xfXfgAYgV6AHni/0TISQgghhBBCiAhR3w7TO0BT4CsgA1jUWAkJIYQQQgghIouqNv4rUtV3Sl4rVVWvq/z5G0VRVjdWQkIIIYQQQggRKerbYTqkKMpMYC0wEDjUeCkJIYQQQgghIok8Ja9udwD3AeOBXcDtdX1A9Z05f9Rm7nBn0HCWaAvDnUKDWTraF+4UGsyc75qHO4UGU/7V3nCn0GC+fenxcKfQYK7ceuZ813jZjTeHO4UGU7ApOtwpNBjn2g3hTqHBaDQRPDfoD2rdzxbuFBqMcUNMuFMQZ6n6rmFaAhwB7gbi8K9pEkIIIYQQQpwFVFVp9Fekqm+HKUVV1YWq39NAamMmJYQQQgghhBCRoL5T8nIVRZkOrAP6A8caLyUhhBBCCCFEJDmb1zDVd4TpJsCOfw1TOXBDYyUkhBBCCCGEEJGiXiNMqqo6gVcbORchhBBCCCFEBDpzHoXyx9V3Sp4QQgghhBDiLCVT8oQQQgghhBBCBJARJiGEEEIIIUStIvmx341NRpiEEEIIIYQQogYywiSEEEIIIYSolS/cCYSRjDAJIYQQQgghRA1khEkIIYQQQghRKxVZwySEEEIIIYQQ4ncidoRJMehJmfsYurRkXHuzOTFjTmCQVkPKnBnokpNw5xzmxONzAYi7eQLRQwegVjjInzILPJ4QZ1+dxqhnyJtTMacnUrzrMGumvhE0TtFpGfr2vfx049xq5R1vHUPGyB58f9XzoUi3TnqjnsfeeJTk9GSyd2Uz574Xa4zV6rTMevMxHp/0BACWOAuz3nocnU7LhpW/8v5fPwhR1kHo9ETd+RiahGS8R7JxLAzcx7Rd+hB14/34CgsAcCyei+/EMaJunYESn4Qv/zCOxXMDPhcqOqOeqxfcS1x6EgW7DrH0gdfrFROszBRrZuKb96PRadm3cis/vbosDDUC9AZiH38SbXIKnuyDWF94psbQqMsnYOg/gNLpD1aVme+agmIyYZtb834ZKr+1/ej0RErqaPtD3r6XlUHafvrIHvwQIW2/Lm6Ph/tmzOa1OU+GO5Xa6Q3EzHgSTZMUvDkHsb1c8z5mGjcBfd8BWGc+iHHsJURdfhW+4iIArE88jGovD1XWQSkGPenzZqJLS8a5J5v86S8FBmk1pL/8MLqURFzZR8mf+ZeqXyXcOA7z0H4cmTQjhFnXTTHqabngEfTpTXDsyuHwA0GOs1oNLeb9GX1qIs6DRzny0LzQJ1oDxaCn+YJH0DdNxrk7myMPBs+/+V8fqtouR6f/FYCMF+/H2CYDz8lSDt35DHjDvHJEb8A87Uk0TZLx5h7EPu/ZGkONF49H33sgticfRIlPxDx1BkpUNK5V/8L55achTLo6rVHP5a9PJaZpIid2H+af9wcei2uL6Tt5DG3P78GHE08di0fOuh59tJGvpv8tJHWIBL6z+JtrI3aEyXLxCLwFhRwdfyeaWAtRA3sHxJjPH4Rrz0GO3XA/2uQkDB3aoGuWhqFtK/JuehD7qvXo0pqEIfvq2lwxCHteEV+MmokhzkzToecGxGhNei78ejZNB1f/nTkjibZXDg5VqvUy4rIRFOYVcucFd2GJs9B7SK+gcQaTgde+nE+vwad+P3zccHL35nL/5Q/SuU9n0pqnhirtAPoBI1CLCyl/6k4UswVt58B9DMD10+fY5zyAfc4D+AqOoOsxCO+Rg9hfuB9NXBKa5m1CnPkp3ccNoiy/iNfGPoIpzsw5g7vWKyZo2aWZFOw9wlvjn6RFn/YkNEsOQ43ANHIUvsITFN8xGcViQd+7T9A4TUoqptFjqpXpOnTE0Ld/KNKsl9aVbf/LyrafVkPbH1tD228TYW2/Ng6nkysnTWHt+k3hTqVOxuH+fax0SuU+1rOGfSw5FeOI6vuY/f13KHtoCmUPTQl7Zwkg9pLz8eQXkjvubrRxFqIHBR6PLSMzcew+yKFrp6FNTsDY0X/M0qWnEHvZqFCnXC8J44bjzi9k39ipaOMsWAb3DIiJGz0Ax65sDoyfji4lEVPn1mHINLj4ccPx5J/kwEVT0MRZsJwXmH/s6IE4dmWTfeVD6JITMHVqTXSfzig6LQevmIbWEo1lcPDzaygZhoxCPXkC64O3oJhj0HWvub0Yhp9qL8YLL8f5w5dYH7kLw4gLwRQVqpQDnHvZIMryilg4diamODOtBwcei2uKic1Iouv46sfipt3b0HZYt5DkLiJDnR0mRVEuUBRlaOXPVyuKcouiKObGTiyqfw8q1m4EwLFuC6Z+3QNi7KvXU7rkE9Bq0MSY8ZXbierfE02shaaLXsbU+1w8R/IbO9U6pQ7qTN7P2wEoyNpJambngBivw80XI2dgzyuuVt5n9vVseu6jkORZXz0GdWfjKv+22bJmC90HBm4bAJfDxR2j7+REfmG18iiz/6CpKAptu7Rt3GRroevYA89Ofz28u7eg6xC8Hvpe52GeMY+oOx4DwLNjPa7vPgGNBqLNqBX2kOX8e20yu7B/1TYADq7ZSeuBgftWsJiaPmc8bdukdWkZiioE0PfohevXDQC4N2/C0D3wQgPActcUyhe+eapAq8V8yx3Y33k7FGnWS9ppbT8/aydpNbT9L4O0/d6zr2dzhLX92piMRpYteZ3U5PDfpKqLvnsv3Jsq97Etm9B3C76PmW+fgn3xm9XKTBdfTty8vxF925RGz7M+ogd0p3yNv5Nq/2UL0f0DL+LKV22geNEy0GrQxlrw2fzHrJQZt1M4d1Eo0603c2Y3rKs2A2BbswXLwMCbQdaVGznxtxWV9TLjs4bvWPx75sxu2Fb7t0v52q2YBwZuF9vKXyl8e3nVdvHa7HgKSzj5zmcAqG53SHOuib5rT9xb/e3Fs30junODt5eoSVOoeP+tUwVuN4rRBFqt/70avuGJlpmdyVntPxbnrNlJiyDnyppiRs26npVzTh2LNTotwx+5mpUvLQ1B5pHFh9Lor0hVa4dJUZRFwJ+AmxVF+QzoDmQAnzR6YnGx+Gz+u3c+WznauJiAGLXCgepwkr7kFbwnS/AcyUebEIe3uJS8mx5El9oEU6/AuwihZkyw4K48kLttFRjjLfX6XKvLBlK88xCle482Znp1mvLM3cz95OWqV2JKIuWV9Sm32omJD9w2Nflh2Q9Y4iw8/uZjuF1uDCZDY6VdJ8USi1rh38fUinIUc5B97MQxnCsWU/7sVJS4RLTtu4HTAS4n5umvoJaVoBaGr1MenRCD01oBgNNmJzrIvhUsJljZlhVZmGKjueaN+/A43eiN4dk2mthY1PLK7WIvR4mJDYgxDh+J5+ABPLm5VWXRV16D41/f4CspDogPF8Pv2r7hf6ztn6mUmNiq0aGa9jHD0JF4sg/gPXRqH/Ps34v97QWU3ncbhoGD0aSkhSznmmjjY/FZfztX2oOfK+3+c2WLD17GU1iM+0g+MRcPw7knG+eB3ID4SKBLiDmtXhVog5xnfJX1OueTOXgKS3AdLgh1mjXSxsfi/S1/qx1tXGDb/y3/Nh+/6N8uhwtw5RyjYuteYkYPRNHrsf28MdSpB6jeXuxoLIHbQn/eCLw5+/EdPrU/Ob5YiumyicTOW4Lr5+/8584wiYq34CzzH4tdtgqighyLg8V0vnQgx3cdonDfqWNx/9svYtsnq7GfLAtN8iIi1LWGqY2qqkMAFEXZoarqJZU/rw4WrCjKbcBtAE+nd+KaxGb/cWLekjI0Fv9AlsZixltcGhCjiYvBZ3dw7Pr7aPr2HEx9u+Mrt+POOQyA+0g+2pTw3+10FlnRx0QDoI+JwlFkrdfnMkb2xJyRRNOh3Yht25T2N49i7zvfNWaqQb0687Vq7x+e9xDmyvqYY6MpDbJtajN32l8oLSrl0ddnUlL4xz7bkFRbGUqUfx9TosyotsBc1HIrnl3+u4TqyQKU2HgUcwyq00H58/cR/eActB26492zJaS5/8ZeZMUY4x8VMsVEUx5k3woWY4g2Bf3csulvYi+ycvVr91IeppOBr7QUxVy5XcxmfGWB28UwYCDalBQMffqibdYC06WXoe/TD73qQ2OxoMQnYhg8FNeqlaFOv5rft33nH2j70RlJpA/tRkwY2/6ZSi0rRYmuYx/rNxBNcgr6XpX72MWX4Vz9E2pZKfh8+E6eQBMfj+94eGcxeIvL0MRUnitjzHiLA9utJj4GtdzBoWsepPni54nq3w3LsH7omqZgHtQbQ+sM4if+iZL3/xnq9GvkKTq9XtF4igLrpY2PwWevYP/lD9Hm789gHtiV8rXbQp1qUN7iMrSnbRdPkO3yW/4Hx/+Z1u8/g3lAV8p/2UbMiH40uekScm99Cnzh/+Yb3+ntJdqMzxqkvfQZiKZJKvoe/dCkN8c49jK0rdthf3Munu2bsDwxF1fWj6iFx0OdPgAVxVaMsf5jsTEmCntx4LE4WMw5I3oSm55E6yHdSGzblN43jqLN0G6oPhVTbDTm5Dg6XNiXPV+uD2l9wkWeklezg4qivK4oymLggKIoTymKMgOoCBasquqbqqr2UVW1z3/TWQJw/LKJqEz/mpKo/j1wrA+8II27cTyWC4aAz4fqcKKYDDh37sN4bgcA9M3T8RzJ+6/yaAj5q3eQPtQ/nSBtUBcK1uys1+ey7l7At+Nms/rO+RRty46YC6ZNWZvpPcS/bXpk9mDLmq31/mzX/l2Z+twU9AY9bTq3ZtfGXY2VZp08uzahq1y3pO3YA0+QTo9h1BXo+w4DRUGT0Qrf0RwMo8ej7z0EVB+4nCj68I2SHcjaTrsh/qkebTK7kL02cN8KFhOsrFW/jlz6zGS0Bh1pnVpweNO+0FXkNO5NGzH07gv4p+e5NweuibE+N5uS+6dQ9sxTePbtwbFiGaUPTqV02n3YFszHtW5t2DtL4G/7Tf/Dtv9dBLb9M4V7y0b0vSr3se698GwN3MdsL86m7KEp2F54Cs/+PTg+X4b5lrvRde4KBgOa5BS8x46EOvUA5Ws3Y65ctxTdvzv2fwcexxJvvpyYMef5O3oVDjRGI3nT5nB44jSOPfgcjh37I6qzBGDL2kLMEP/UL0tmN8rXBp5nkm8dR9yFv9XLiSZMo+LB2NZsqVq3ZMnsRvkvgfk3ueUyYseeyl8xGdE1iafJbVeQM/kJfOVBL7VCzrNtI/rKdUv6rj3xbN8cEFP+ytNYH51C+dyn8B7ci/OrZShR0agul7/TpxLWc2VO1g5aV67xbZnZhUNBjsXBYj6buoD3xs9mxZT55G/L5tfF3/H+lU/zwdXP8K+n3mP/D5vPms7S2a6uDtPNwKfA25WjS/uAEmB8YwjGuU0AACAASURBVCdm/eIHtClJZHzyBt5SK+7DeSQ+eGu1mLIPPyNm3AWkv/cK3pIyKrJ+xbllF77iUtL//irunMM4t+9p7FTrlP3pGqLSErjoX8/iLLFhzTlOr8evCXda/7Eflv1IUloSb3z7OtYSK5tWbyKteSq3PnpLnZ9d/+N6DEYDL3/yEh/M+zsOe/iG6N3//gElIQnzrDdQy634TuRhHF99H3P9+Bn6QaMxz5iHZ1MWvrxD/rLzLiD64VfwlZfh2fFrmGoAW1ZkEZOawD1fPU9FiY2i3ALGzLi21pgDWduDlu39aQs6o55bP5rFT/OX47I7w1Inxw/foWnShIT/W4hqteLNO4r5tjvDkst/K+fTNUSnJXDhv57FVdn2e/4Pt/0zhfPH79AkNSFu/ql9LHpy3ftYxUfvEX3T7cS9OJ+Kvy9BtdlCkG3trP/8EV1qEq1WLPCfKw/lkfxQ9WNxyfufE3vFaFp8OBdfiZXy1eE7ZtVXyYqf0Kcm0e6reXhLbDhz82k6Y1K1mMIlX5B45Ujafvoi3uIyrD9HzgNHSlf8iC4tiXO+fBVviRVXbj5pj1TP/+S7X5AwYRRtlr6Et8SK7eeNxF8xAl1yAq0WP0Xrj14gfkL4H8rh+vlfKInJxMx9G5/Nii//KFE31N1eHMs+IPqmu4h5/nX/dL288N1g2LF8DTFpCUz++lkcJTaKDx3n/JnX1BqTk7UjTNlGLl8IXpFKUeuxCE9RlCRVVU+e9n6Cqqof1/aZg11HnzEPH1xzMiXcKTSYJdrCuoP+RywdE8lN64+Z8134p442lHvbnTnrbr7d0TzcKTSYK7c+Fe4UGkzZjTeHO4UGU3ggOtwpNBinI2K/qeQP02jOmEsYMnqGv2PfUN7Y8N/NXookj+S+9z83v+271KsavWGMKvhHRP5d6vtY8d9/IcvUhk5ECCGEEEIIEZlUlEZ/RapabwdVPk58GNBCUZTHK4stQFEj5yWEEEIIIYQQACiKYgKWAs2BrcAN6u+myimKogCLgA7AceByVVU9/+3/u64RphzgJ6AMWFn5WkoI1jAJIYQQQgghIkMErGG6Djiiqmp3IAEItshvEKBTVXUAEAuM/g+qGqDWESZVVXOBXEVRFqqqGv5HTgkhhBBCCCHORudz6rtgfwCGA9/+LqYA+Gvlz66G+h/Xd4XmN4qiPAxUPRNSVdUzZwWxEEIIIYQQokaheNTW6d/pWulNVVXfrPw5Cfjti8DK8E+7q0ZV1X2V/85l+Pst3zREXvXtMC0FnsA/F1AIIYQQQghxFgnFQxkqO0dv1vDrQiCu8ue4yvcBFEW5BLgX+JOqqt6GyKu+HaYTwLKGWDQlhBBCCCGEEH/Q9/jXJH2Cf3reX34foChKGvBnYIyqquUN9T+u72PFNwA/KYpyl6IoNyiKckNDJSCEEEIIIYSIbD6l8V91eB/IUBRlK/4ndh9QFOWl38XcCDTFv5xotaIok37/j/wn6jvCtK3ypUIEPyRdCCGEEEIIccZRVdUJXPy74mm/i3kBeKGh/9/1HWH6ADACvQA9/h6eEEIIIYQQ4izgQ2n0V6Sqb4fpHfzDW18BGfi/EEoIIYQQQgghzmj1nZLXSlXV6yp//kZRlNWNlZAQQgghhBAisqjhTiCM6tthOqQoykxgLTAQONR4KQkhhBBCCCFEZKhvh+kO4D5gPLALuL2uD2R88vh/kVZk+XX0/HCn0GBWFewKdwoN5tjKLuFOocEcxRnuFBqM5YErwp1Cgxn82Ipwp9Bgym68OdwpNJjYxe+EO4UGE/3L8nCn0GB869aFO4WGY6jv5VHkU/TJ4U6hwUSvd4c7hbNaKL64NlLVdw3TEuAIcDf+L4o6c85WQgghhBBCCFGD+t5CSVFVdWHlz08rirKqsRISQgghhBBCRBafErlPsWts9e0w5SqKMh1YB/QHjjVeSkIIIYQQQggRGeo7Je8mwI5/DVM5cENjJSSEEEIIIYSILGoIXpGqXiNMld+s+2oj5yKEEEIIIYQQEeXMeQyMEEIIIYQQolHIU/KEEEIIIYQQQgSQESYhhBBCCCFErXxn70PyZIRJCCGEEEIIIWoiI0xCCCGEEEKIWvk4e4eYZIRJCCGEEEIIIWogI0xCCCGEEEKIWkXy9yQ1NukwCSGEEEIIIWp1Nj/0IWI7TE6XmwefW0B+YRHtWzXjmQduQVGqb6kyWzn3PfMaHq+XQb3O5far/4TH62X6i//HiaJSWmWk8tS9k8JUAz+dUc9NC+4nPj2JvF2HeP+B1+odp9FquH7eVOJSEzh+8BgfPvR/QcvCwWg08sEHr9OsWVO2b9/N5Mn31xo/Zcpkxow5n4sumojJZOS99xaQlpbM5s07uOeeR0KUdXWKQU/GqzPRNW2Cc08OeX9+KTBIqyF97nR0KUm4so+QP+OV4GVhpDPquXvBNBLTm3BkVy5vPTCv3nGGKCN3zLsPS0Is+zbs5uPn3yUuOZ5b507FZInilxWr+NeiL0Nco1Ocbg/T3vmaghIb7dKTeHriyIDjQIXTzSPvfktxuYMerZty/yWZYco2CIOe5BeeQJuajHv/QU4+/nxgjFZDk2ceRZuchDv3MEVPvYRiMpH0zAy08XE4t+ygZN6boc/99/QGYmY8iaZJCt6cg9hefqbGUNO4Cej7DsA680GMYy8h6vKr8BUXAWB94mFUe3mosv6PuT0e7psxm9fmPBnuVGrldHuYtvi7U23kmuEBbWT9/mO89tV6APKKrdw9ti/DurTi/kXf4PH6GNSxObeN6h2O9KvT6TFedT9KXBJq/iGcnwY/X+rP+xPaDr1RXQ6cH7wIXi/ojRgvvwvnP/4S4qRroNVjHD8VJTYR9fhhnCveCBqmH3gR2na9/HX5aG5lPe5BMUbjzd6Oe+XSECcehFaHcdzdKLGJ+I4fwfXFW0HDdP3Goj2nB7gdOD+ZBz4vAPoR16Lojbi+fieUWVejNeq56I2pWJomUrj7MN/eF7g9gsVkDOhE5p/HAxCT0YS1Ly1l19JVAPScPIZWI3qw7Nogx3VxxonYNUyf/7iW1CYJLH31ScpsdtZu2hEQ8+XKf9O2RTpL5jzCpl37OZJ/gh9+2USH1s1ZMucRThSVsvvgoTBkf0rvcedRmn+Sl8ZOJyrOTIfB3eod13V0X47tymXe+FnEpiSQ3rll0LJwuOaayzh6NI/+/ccSHx/HiBGDa4xt0SKD664bX/V+3LgL2b59N+eddwkjRgymY8d2oUg5QOyl5+POLyTnknvQxlqIHtQzICZm5ECcu7M5dM00dCmJGDu1CVoWTpnjhlCcf5JZYx8kOs5Ml8Hd6x03cNwQDmzay7PjZ5LRvjlN22Yw8qYLWfXxDzx92SMMvmoExmhTiGt0yhcb9pIab+Gjh66mzO5k7Z7DgTG/7qVryzQW33sFB/KLOJhfFIZMgzOPHYXn+Anyr70NTUwMpv6BF6VRw87Dte8ABZPvRZuUhL59W6LHjsC1bRcFk+9F36YlulYtwpB9dcbho/AVnqB0ymQUiwV9zz5B4zTJqRhHjKlWZn//HcoemkLZQ1P+JzpLDqeTKydNYe36TeFOpU5f/LqP1HgzH02b4G8je48ExPQ9J51FUy5l0ZRLaZeeRMeMJny5cR9tUxNYPGUcm7PzOXqyLAzZV6frdh5q2UkcC6ZDlBlt28DzpZKQgpLcDMfbT+DduxklNgklPpmo259Gkxae82Ewuq6DUMuKcLw1E0xmtG3ODYhR4pP9dVkyG++BLSixieh6DsOzLQvHoifQdewLpugwZF+dtksmPmsxjndmoZii0bTqEhCjxCWjaZKB84Pn8B7chhKTCICmaWu0bbqGOuUAHS8bhC2viA/GzMQUZ6bF4MDtESzm6C+7+PiK2Xx8xWwKdx3mxPYcAGIykug0oebrnjOVLwSvSFVnh0lRlCGKotyrKMrDiqJMUhSlVeOnBeu27mZAj84A9OvekfXb9gSNK69woKoqqCp7sg9zXq9zuf7S0Xi8XqzldizRUaFIt0btMs9lz6ptAOxbs51zBnaud9yulZv56W9foNFqiIqNxmmtCFoWDsOGZfLDD6sBWLlyDUOHDqwx9qWXZvH443Oq3u/Zs48PPvgUAJfL1biJ1iJ6QHfsWf4LovJftmAeENjRsK36laJ3loFWgzbGjM9mD1oWTp0yu7Jj1VYAdq3ZTqeBgSeCmuI8LjcGkxEAvVGPx+2pKtPqtAD+9hUm6/YdYUCH5gD0a9eMDfuOBsQYdFocbg+qquJye9BX5h0JTH174Pj3rwA4NmzC2KdHQIxjzTqs7y0FrQZNjAVfuR1cbpTK7aIYDOB2hzTvYPTde+HetAEA95ZN6LsF3mAAMN8+Bfvi6iNiposvJ27e34i+bUqj59kQTEYjy5a8Tmpyk3CnUqd1+48xoH0zAPq1y2DD/mM1xla43BwuLKV9ehIAdqcbVVVRVdhz7GRI8q2Nts25eA/4z4Pe7O1oWgeeL7VtzkWJMmOaNAtty46oxcdRS05QMf/PoU63VtpWnfFmbwfAm7MTTcsgdWndBcVkxnTDo2ibd0AtOYFn0094d/4COgNoNODxhDr1wDxbdsKX479p7T20C22LTgExmladwBSN8dqH0TRrj1p6AjRa9EMn4F71aahTDtA8szOHVvm3x+GsnTTLDNwetcXoTAbiW6VSuNt/027oE9ez5oWPQpC5iBS1TslTFGV+ZYwCRAOJwB2KonysquqLjZlYqdVGjNl/Z8UcFUXOkYKAmAuHDiBr43YeeG4Ber0Oh9NFdJT/bvi1Dz5NckIczdKSGzPNAFfMnkR6x1N3g70eLw6r/4LaYasgpW160M+ZEywBcS67E4D7lj9N2fFiTh4+XhUfrKwxvfLK03Tt2rHqvdvtobTUf0eyrMxGu3bBR1muuupStm7dxa5d+6rKNm3yH5DuuWcSmzZtZ/fufUE/29i08TF4bf673T6bHU3rjIAY1e4AoOXHf8Fzogj34fyq3wUrC4XrZ99Ks46n7qR6PV4qqvYdO01r2McsCTEBcb+sWMXMT5+l70WZ7MzayolDBXy78Atm/XMOF999OT++/y2uCmfjV6oGpeUOLCYDAGaTgZzjxQExY3u348ZXPuG7zfvp174ZzZvEhTrNGmniYvGdto/pWjYPiFEr/PtY6qL5eAuL8B7Nozz/OKnvvEr0yKE41m3CczQvpHkHo8TEVo0OqfZylGaBdTEMHYkn+wDeQ7lVZZ79e/HmHMSzZyfxb3+IY/nH+I6Hts2cyaq1EaOenOMlNcb+svco/dr5j3MX9m7H2j1HeHDxt1U3HcIu2oLqqLwB5ahASQo8linmWNRyK84PXsJ061NoWnbEl7s7xInWQ5QF1VlZF2cFSlLTgBAlOhbVXobzo7mYbpqFpnkHfIf3gKIQdfcreHetA0/4bipW5RllQXX6b86qTgdKYpC6RMVAhRXnp/MwXjcTTbN2aJq1x7NjDWp5+EcvTQkWnJXnP5etgoS2gXWoLabF4HM5nOXvNHa4dCCFuw5xMsgNvDPd2fzQh7pGmLqrqnqHqqq3A/Gqqj6kqmo/YEKwYEVRblMUZYOiKBv+9o/P/qvE4mMtWMv9O67NXkF8rCVo3JNTb+YvM+7GoNeRFB9LSZkNl9vNu3NmUGazs25raA+knzy2kFcnPFH1KisoxhTj7/hFxURTXmQN+rnyImtAXHS8Ba1Bx18vf4yoODPnDOwctCwU7rvvUUaMGF/1yssrIC4uFoC4uBhOngy8kAUYO3YEw4cP4t13X6Vnz67ccceNANx663UMGtSPW255ICT5B+MtLkNrMQOgjYnGWxx4UNfEx6DodeRe9aB/2l7/bkHLQundx97iuQmPVr1KCoqIOm3fsdawj1mLygLiLrrrcn56/1tmjJiKOT6Gc3p14JrHbmLJzDd5ePgUuo/oTWJ6+O6yx1tM2Bz+Cwabw0m8JXDEeOF3GxmfeS7LZ0ykrNzJ5uzwdy5+4yspRVO5j2ksZnwlpQExmrhY0OspmDQVTYwFY+8exN58LbZPPidv/M1o4mIwdAtNO6+NWlaKEu2vi2I24ysLrIuh30D03Xthmf44unM6YLr4MnwnCvDs2Qk+H76TJ9DEx4c69TNavPn0NuIi3lzzFNqfd+Qy5LRp3LOuGsrcmy5Ar9OSGKRthVy5FeW3KWimaFR74LFMdVSgnvSPoqlFx1FiEkKZYf1VWFGMlXUxRgWvi7MC9aT/eKWWVNbF7D+vVsx/AO053VHiQ3vTNxjVbkUx+vcPxRgFQeqCy4HvpP9GiFpyAiUmAW2brujOHYRhxLVo23ZD2yH4NN5QqCiyYqw8/xlioqgIcp6sLab1yF5kf++fkdJ6RE+aD+rC2Pn3kNK1Nd1uHBWCGohwq6vDVKQoyj8VRfmp8uemiqJsALYFC1ZV9U1VVfuoqtrnlqsu+a8S69+9E2s37QRg3dZd9OvWMSDm1x17mf3aElxuN3uzj9CtQxsWL/+Gb1dvQKvVYDIacIZxyhfA3qztdBjiv6Bul9mF/WsD12LVFDfs1ovpceEAVJ+Ku8KF3mgIWhYOP/6YVbVuaejQTFauXBM07qabpjJixHiuv34KmzZt4403FtO1ayfGjDmfiRPvwhPG6Qb2tZuJPs8/rSh6QHfs/94aEJM46XJixg72X+w5nCgmQ9CycNqVtY1zh/inE3bK7MrutdvrHWcyR+F2+qd7eVxujGZTZZkL1ecDVUVv1IemIkH0b9eMtbv96xDX7TtK33MCRwHLnS6Mev80PL1Oi90Z/ulrv3Gs34RpgP8iwdS3B44NmwNiYiZOIHrkUPD5UCv3J010FGrlsUt1udFEhf9i1r1lI/pefQH/9DzP1sD1PbYXZ1P20BRsLzyFZ/8eHJ8vw3zL3eg6dwWDAU1yCt5jgWtsxH+uf7sM1u7x/03X7T9G33OCjzCrqsr6A8foV/n7jQfyeGbpKlweL3uPnaRby9SQ5VwT78HtVeuWtG264M0OPF/68g6iSW8LgJKUhlocmlkWf5Q3e0fV2h1tqy54c3cGxPjystE09c/OUBJSUUuOYxx1HZpm7cDr9j/MQhe+4+9vvLm70LT2T/XWtuyE91DgjWhffg6apq0B/zozteQEzg+ex/n3F3B9/wHeA1vx7tkQ0rxPdzhrBy2G+LdH88wuHFkbuD1qi2mW2YnDa/zvv566gI+vmM1X98zn+LZsti7+LgQ1iAw+pfFfkaquDtMVwOvAHGASUArcrKrq5MZO7KJhAzh+spgrpswizmKmWVoyL739j2ox5/U+F5fbzU3TX+DWqy4mOsrE1Redz7J/rea6ac8QH2shs2fw9Ryh8uuK1cSlJvLnr17AXlLO3qztJDZL5pIZ19UZl7XkG/pfOYx7P32K8mIru3/eErQsHD78cDnp6WmsW/c1xcUl/PhjFi1bNue552bW+dlbbplIy5bN+O67j/j++6WMHj2s8RMOouyzH9GnNqHVZ6/hLbXhPpRH8vTqu3bJ+58Td8VoWv7jZbzFZZSv2hi0LJzWrviZ+NREnvpqLuUlNnZmbaVJsxSumnFDnXHfv/sVw6+7gJmfPovBZGBn1ja+eH0ZVz96E48tf55DO3MoCOOIzYV9OnC8tJwJL3xIXLSR5k3imLsiq1rMVed15aOs7dzwl6U43R76V67niATlX32PLrkJaX9/C1+ZFc+RY8Tfe3u1GNvHK7BcMobUha/iKy3DsXYD1o9XEDP+T6QufBXFaMARAQ8fcP74HZqkJsTNX4hqteLNO0r05Dvr/FzFR+8RfdPtxL04n4q/L0G12UKQ7dnjwt7t/G3kpY/9bSQplrmfrQ2I2374BG1TEzDq/TPxB3VqjtPj5eb5K7h1VC+iw3hj5DeeratRYhOJuusFsJejFhVguKD6udJ3eB+q3Yrp9mfwFR7Dd/RAmLKtnWf7GpSYBKJufRYcNtTi4xhGXFMtxnd0P2qFFdOkp/CdzMN37CCurM8wnH81pklP4dm/GbWw5jVpoeLduRaNJR7TzU+hVpSjlhxHP/yqajG+YwdQK6wYb3gctSgfX152mLINbs/yNVjSEpj4zbM4Sm2U5h7nvJnX1BpzeLW/w57avQ1Fe4/ijaCbcSL0lPos6FYUJUlV1ZOnvZ+gqurHtX3GuXf1GTPV8eHR88OdQoN5oyDwRPq/alPzwCf1/K96wRX+EYSGsuD1oeFOocGceGxFuFNoMObUCFij0kBiF4fv8cQNzfPL8nCn0GB869aFO4WGY4jYb135wxR9+DvDDeWt186cTsu9h96L4PGU4N5qdl2jX9vfeiQy/y71faz4st+9n9rQiQghhBBCCCFEpKnrKXlDgWFAC0VRHq8stgCR80UnQgghhBBCiEYVyd+T1NjqGnPOAX7Cv5ZpZWVZBRD+CfVCCCGEEEII0chq7TCpqpoL5CqKslBV1ZW1xQohhBBCCCHOTGpEri4KjfquavxGUZSHgarnJ6uq+lTjpCSEEEIIIYQQkaG+HaalwBNAZH7hgRBCCCGEEKLRyBqmup0AlqmqeuY8l1YIIYQQQggh6lDfDtMG4CdFUT4AbACqqi5ptKyEEEIIIYQQEUNGmOq2rfKlAmfxki8hhBBCCCHE2aS+X1z7AWAEegF64P1Gy0gIIYQQQggRUdQQvCJVfTtM7wBNga+ADGBRYyUkhBBCCCGEEJGivlPyWqmqel3lz98oirK6sRISQgghhBBCRBbfWbwop74dpkOKoswE1gIDgUONl5IQQgghhBAikshDH+p2B3AfMB7YBdxe1wd8OVv/i7QiSzNfff9MkS8tOj7cKTQYe4U+3Ck0GIO2vrNjI5+S2ircKTSY2HZnzumhYFN0uFNoMNG/LA93Cg1GN2BcuFNoMJ78I+FOoeFoz5zzPr4z5zhmV/LDnYI4S9X3Km0JcAS4G4jDv6ZJCCGEEEIIcRbwheAVqerbYUpRVXWh6vc0kNqYSQkhhBBCCCFEJKjvmHOuoijTgXVAf+BY46UkhBBCCCGEiCSR/NjvxlbfEaabADv+NUzlwA2NlZAQQgghhBBCRIp6jTCpquoEXm3kXIQQQgghhBAR6Gx+rPiZ82guIYQQQgghhGhgZ9BzM4UQQgghhBCNIZKfYtfYZIRJCCGEEEIIIWogI0xCCCGEEEKIWslT8oQQQgghhBBCBJARJiGEEEIIIUStfGfxGJOMMAkhhBBCCCFEDWSESQghhBBCCFGrs/kpeRHbYXK6PUx7+wsKim20y2jC09ePRlGqf2NWhdPNI4u/pthWQY826dw/7ryq8sfe+5aXJl8UjtSraI16/vTGVGKaJlK4+zBf3fdGvWP63nERbUb2wl3uYPktc/G5vQAMf+J69FFGvp3+t5DW5XQGo4EF77xMekYqu3fu44E7ZwaN02q1zHvreVLSkjm4P5fpU2cFLQsXxainzRvT0ac3oWJXDrn3vRI0ruXcezG1zcB9spSDtz6HxqCn1fwH0SXEUr5hF0efXRzizKvTGfXcvuBBEtKTOLrrEO88EPw7pmuKG337JXQb0Qen3cGCW+cw6MrhjLr1EspOlADw6qTncFjtIauP0+XmwVfepeBkCe1aNOWZu64OaPt2h4tH5r9PibWcHu1bcf/EizlRXMajCz6k3OFk7KCeTBxzXshyrpFej/m+J9EkpeA9dAD7gudqDDWOHY+u5wDKn52GJjmN6CmPgari+vELXD99FcKkg1MMetLnzUSXloxzTzb5018KDNJqSH/5YXQpibiyj5I/8y9Vv0q4cRzmof04MmlGCLMOzun2MG3xdxSU2GiXnsTT1wwP2MfW7z/Ga1+tByCv2MrdY/syrEsr7l/0DR6vj0Edm3PbqN7hSP8Pc3s83DdjNq/NeTLcqdSb0+Nl2oqNFFgraJccy9MXdg/cRodO8trqPQDklVVw93kduOTcZuFIN4DT42XasvUUlFXQLiWWpy/uFZh/biGvrdoFQF6pnbuHdOKSri1we308+Ok65k0YEI7UAzg9XqYt30CB1UG75BievqhnkG1RyGurdgOV22JwRy45t7m/Lss3MO+KfuFIvYrWqOfy1/3XWid2H+af9we/Hqsppu/kMbQ9vwcfTnwenVHPuAVTsCTHk78jh68fWRjKqogwidgpeV+s301qvIWPHplImd3B2t2HAmM27KZrqzQWP3AlB/JOcjC/iKOFpUx86UP2HDkRhqyr63zZIGx5Rbw7ZibGODMtB59br5i4FskktW/GP8bPJvunLcQ0TQQgrXsbWg3rFupqBLhswkXkHyvgwqFXEhsXy+BhA4PGjb5wOLt27GXChTeRktqETud2CFoWLomXDcOVd5LdF9yHLs5CzJAeATHmvp1QdFr2XPoQWksUsUN6knDZUMo37mHv5Q9jat8C0znhPUH3HzeY4vyTPD32z0THmek0OPg+EiyuSfMU0ts356UrH2f7T5tISPPva//8yz94ccJjvDjhsZB2lgC+WL2R1MQ4Pn7hAcrKK1i7bW9AzJdZG+l2TksWP3kPB44UcPBoAX//Jotxw/ry3uwpLP9xHXaHM6R5B2M4bxS+ohNYH74FxRyDrmufoHFKk1QMQ8ec+tyoS3Gv+xnbE1MwjBoHBmOoUq5R7CXn48kvJHfc3WjjLEQP6hUQYxmZiWP3QQ5dOw1tcgLGjm0A0KWnEHvZqFCnXKMvft1HaryZj6ZNoMzuZO3eIwExfc9JZ9GUS1k05VLapSfRMaMJX27cR9vUBBZPGcfm7HyOniwLQ/Z/jMPp5MpJU1i7flO4U/lDvth5lNQYEx/dNIQyh5u1OYUBMX1bJLHo2kwWXZtJu+QYOqbEhiHT4L7YcYTUmCg+mjy8Mv/Aa5K+LZuw6LrBLLpuMO1S4uiYGofD7eXaRSv5JUh8uFTV5eahNdelRRMWTTyPRRPPo11yLB1TKuuy5OeIqMu5lw2iLK+I+G2Y9QAAIABJREFUhWNnYooz0zrI9VhNMbEZSXQdP7gqrsOF/Ti++zCLLnmc1oPPJaldesjqEW5qCF6RqtYOk6IoRkVRblEU5X1FUb5SFOVdRVEmKYpiaOzE1u09zICOLQDo1745G/YFntAMOi0OlwdVVXF5POi1GjKaxPHpzOsbO716aZ7ZmdxV2wE4nLWT5pmd6xXTYlAXTHFmrvr4UTL6daD00Ak0Oi2DH7marJeWhrQOwQwc3I/VK9cCsHbVOgYM7hs0buUPWfxtwbtotVpi42KwWW1By8IlZlBXrKs2A2Bds5WYgV0DYjwnSji+8J8AqG6P/78uN5oo/wWsYtTjqywPl46ZXdm1aisAe9Zsp8PAwBNBTXEdB3UlOtbCtH88Sbu+nSg8fByA4TeOZeYXc7hy1s2hqcRp1u3Yz8Cu7QDo16Ut63ccCIgx6HRUuFyoqorT7UGv02LQaalwuXF7vJVRSsDnQk3XpReebb8C4NmxCV2XwE45QPQN91Dx4VvVyhRTNCgKisGANr15o+dal+gB3Slf47/otv+yhej+gR3z8lUbKF60DLQatLEWfDZ/Zztlxu0Uzl0UynRrtW7/MQa099/o6Ncugw37j9UYW+Fyc7iwlPbpSQDYnW5UVUVVYc+xkyHJ979hMhpZtuR1UpObhDuVP2TdoZMMaOnPuV+LJDYcrvlvXeH2crjYTvsI6jCtyz3BgFbJAPRr2YQNuYEdvt9UuD0cLrbRPiUOk17Lx5OHkxoTFapU67Tu0EkGtKrcFi2bsOFQbdvCw+HictqnxPrrcvMwUmNMoUq1Ri0zO5Oz2n+tlbNmJy0GBl6P1RQzatb1rJzzUVXcyX1H2f7pagC8Lm/AvyPOTHVNyfsUOAgsBcqAOOB84AqgUee7lZY7sJj8F6Vmk4Gc48UBMWP7dODGlz/iu0376NehOc2T4xszpT8sKsGCs/LuvMtWQULbpvWKcdkc2E+WsXzyXK5ZNouMfh3I6NuBnZ+sxl4Y+juaT704g46d21e997g9WMv8HR2b1Uabc1oF/Zy9vAKAZd++x/GCExzOPVr1u2BloaZLiMVb+bf3WiswtskIiHHm5AEQN2YAil5H2cpNoFHosGIOCRcN+n/27js+qipt4PjvzmQymZLeIBACBJAiCEgNRQFRQUVRWHctWFBREewiYgXUtbH2gopYcF1fV0AXuyKdBaQ3EUISSgrpM8mUzMx9/5iI4MwkYc0U4fn6mY+Zw5PkuTn3zj3nnnPuxbJqK878opDm/bdZN9C6c9bR926XC1v9dtistaRn+7/aZUo0+8Q5rDYs5dW8euNTTPv0cTr07Uz+tlwO7S4gd9MvPLHiFb5/+z+UhXDEttJai9noPcGaDTHkFfr+7lGDejHh4Zf59r9b6d+tA5npKVw5egh/e+AF3l70PePPGYgxJujXdRqlmONQa73HimqrQdPSt+OjyxmBu2AfnoN5R8sc3yzCeNO9GG+ehmq1gC78I0zahDg8lhoAPNZaotv5jqyqtXYA2vzrH7iOlFN3sIjYC8/G8fN+HPvyQ5pvQ7znF+/+YdLryCupDBi7ds8h+nX0fjaMPrMja34+yN3vfuO9YBfmiyUnsyqbE7NeB4BJH0VeeU3A2LV5R+iXlRyq1JqkyubEHONtYpn0OvLKA18cXLv/CP2yUkOV2gk7ri6ioxrelrxS+mVFXufckGDGUf1bWyu5vZ/2mJ+YrhcPpGRXAaW//NZWKdqeB0Df68+jaPt+yn4JfMHlZHMqr2FqbEpea1VVp6iqulBV1e9VVf1UVdXbAL+XOxVFuUlRlA2Komx4+4uVfyixBLMBa/2UGqvNSYLJ92rLvG82MG5wdxY9NIHqGjubcyNrp7WVW9DHGgGIjjVgK7c0KcZptVGR622oVxWUYE5PpN3ZPeg2bgjDHrmKdiN60nG0/1GdYHj43if4ywXXHn0VF5UQG2cGIDYulopy384sQEJiPNHROi47fwLx8XEMGNzXb1m4uMqr0db/7bVxRtwVvvUDED+yH2nXX8i+6x4Hj4cWk8dR+v5X7Bw2GW2CGdOZnUOZNv986K2j0+WeGf8QlcUVGOq3wxBrxOpnPwOwllt84mxWG8W53hPBkYJiElokUX64lNxNv6B6PFQUlRGbHB+aDauXGGvCWt/wttTaSYw1+cS8vfgHxp8zgMXP3UeV1cbmPXk8897nPDjxUj77xzSWb9xFYan//TKUVEsVitF7rChGE6qlyidG13sgUd16Y5z6MNp2nYg+9xJw2Kl9/hHvmiedDrU6/NvirqhGU18XmlgT7grfizeahFgUnY6Cv92NNj4WQ/8emM/uh3FATzKem05Mtw4kXHlRqFP3kWCKwWp3AmC1O0kwBb4CvnxHPkO7/naB4pHLz2LOteehi9KSZI6cUYCTTYIhGqujDgCrw0WCURcwdvm+EoZmp4cqtSZJMOix2r0daqu9jgRD4As4y/cWMbRDi1CldsJ86qLBbSmOuLoAsFVY0Md5z3/6WAO1fs73/mI6jOhF1qBuXPzSbbTo3o4zr/FOLe511Qha9zuN/9z1Rug2QoRVYx2mnxRF+UJRlDvrp+LdqSjKl8B6f8Gqqs5VVbWPqqp9Jo7+Ywuu+3fKPLpuad2eA/Tt6Hs1s8bhRK/zXsHRRWmprT+gI0XBqh1kDfVO82qT040Da3Y2KaZ4237Sz/DO/U9om05VQQn/Gj+bjy9/nKWPfcD+7zfzyxd+qyAkVi9fx5CzcwAYOKQva1b6z+WGyRMYffG5eDwebDY7MTF6v2XhYlm15ei6pdicHlhWb/OJiUpNIP3mS9h3zSw89SNmWrMB1eFtbKnOOjQNNLZCYfeqbXQdegbgnXb385rtTY4r2J5L2x4dAEhr24LSghLGP3gNHfp2RqePJikjhZL9haHZkHr9unVgdf26pfU79tK3a7ZPTK3dQXT9sR+t01Jrd9SX6dBqNCiKd2F/uLl2bDy6bimqW29cO33XkdS+PBvrY1OpfXEm7v17cH6zCF3OCPSXTkCJjUeJMeApDv/FoJo1mzHVr1sy9j+D2v9u8YlJuu5SYs8fDB4PHpsdjV5P4T1Pc+DKezh895PYd+ylcsHnoU7dR/+OrVjzs3ea97q9h+nbwf+orKqqrN93mH71/75xXyGPf7ICp8vNnsNl9MiKvIbhyaJ/VsrRdUvrCkrpm+l/1EJVVdYXlNGvTWSNMHnz905xXldQSt8Aoy7e/CNzVOZX3m3xjvSvKyilb5sGtuVAZG5L3qodtBvibWtl5XSjYLVve8xfzGdTX+WDcbNYPOVlirbt56d3vyWtSxs6DO/JoltfwuM6tabkeZTgvyJVgx0mVVWvB54DUoB+QDLwtKqqE4Od2Og+p1FSaWX8kx8Qb4ohMyWeOQtXHBdz+ZAz+HjFViY89y8cdS76nxb+ef7H2rVoNeYWiUz4+gnsVVYq80s4a8bfGowpWLmDwo17sZVbuPLzmZTnFlK0JTdMW+Df4k+WkN4yjS+X/x9VldWsWvZfWrdpxQOP3XVc3Ptv/4vxV17Cv796j8qKKpb/sNpvWbiUL1xGdItkunzzAq5KC478Qlo9eO1xMcnjhhOVlkSHDx6l07+fJPnyERx59wtSrh5Fp0VPoYnRY1m5NSz5/2rd4hUkpCfx0JfPUlNpZfeqbSS3TuOyB65uNC534x6sFRamL36Son2Hyduyly9f+ZRLp13JvZ/MYsmLn1BbHXgqTDBcMLg3JeXVjLvvOeLMRlqnp/DcB8c3si8/N4f/+24NVz/8Enani/6nd+T6McN49oPPueLBFzktK4O2LcM/xcW58js0SSnEPvUWqrUaT/FhYq68udHvq1v5DVHZnTHd9yS2d/3f9TDULJ8vJSo9mbaLX8VdZaGuoJDU+244LqZywX+Iu+xc2nw0B0+lhZqVP4Up24aNPrMjJVU1jH/2/4g36slMjmPOZ2t84rYfOEJ2euLRC3ODumTicLm57uXF3DiyN0Z94FEP8ceM7pJBidXO+HeWEx8TTWaCkTlLfRu524uqyE4xo4/ShiHLwEZ3a02Jxc74t5cSH6MjM8HEnB98L2ZtL6wkOyUu4vI/1uiurbzb8s6Pv23L0h0+cduLKslOjo3IbdmxaDWxLRKZ+NUT2CutVBSUMPx37bHfx+St8t1GgF5XDie+dQpXfvwgV33yEO0j4GZcoeJBDforUimqGpzkbN+8GrlbfYJevSF8jfrm9nKN7yjKn9W/jW3DnUKzeUMb/vU2zeWFTyPjpivNwf7MPxoP+pMo3hT+hdfNpc2zkXPHvT8qasAl4U6h2bgWvRzuFJqPNmKfunLiPCfPypPnZ4Z2zXAwTc//IILHU/x7sO0VQW/bz877MODfRVGUGLz3VcgEtgIT1N91ZJoS879o7C55GYFef/QXCyGEEEIIIf4cIuC24lcBB1VVPQNIBPxdPWtKzAlr7BLKI8DVwFqOv0evivdueUIIIYQQQggRbMOBf9d//QMwDPjmf4g5YY11mG4GOqqqKp0jIYQQQgghTlGhmNypKMpNwE3HFM1VVXVu/dfJwK+3m60GTvPzI5oSc8Ia7DDVz/mTzpIQQgghhBAiqOo7R3MD/HMp3mfCUv9/f0+EbkrMCWvstuIAKIqS/Lv345vjlwshhBBCCCEiXwTcJe974Nz6r4cDS//HmBPWpA4TsPB376c2xy8XQgghhBBCiCZYALRSFGUrUA7sUxTl2UZivm+OX9zglDxFUc4CzgbaKIrycH2xuT4BIYQQQgghxCkg3M8LUlXVAVz4u+J7mhDzhzV204c84EfgMmBZfZkN8H1cvRBCCCGEEEKcZBq76UM+kK8oyjxVVZc1FCuEEEIIIYQ4OZ08j0A+cU19lPXXiqLcD0T/WqCq6szgpCSEEEIIIYQQkaGpHaZPgEeBkuClIoQQQgghhIhETbiL3UmrqR2mI8BCVVVdwUxGCCGEEEIIISJJUztMG4AfFUX5ELACqKr6XtCyEkIIIYQQQkSMU3d8qekdpm31LxVQmvINnmXN8pyoiJDg0Yc7hWZznfn0cKfQbLa4mrQr/ikUKZXhTqHZqPk7w51Cs9Ffcna4U2g2jjUbwp1Cs/GsWxfuFJqNq+hguFNoNlGX3BbuFIQflonXhTuFZqOQGe4UxCmqqQ+u/RDQA70BHd6HQgkhhBBCCCFOAZ4QvCJVUztM7wAtgS+BVsD8YCUkhBBCCCGEEJGiqVPy2qqqelX9118rirIyWAkJIYQQQgghIot6Cq9iamqHqUBRlBnAGmAgUBC8lIQQQgghhBAiMjR1St7NgBYYB1QDk4KWkRBCCCGEECKiyBqmxr0HHAQmA/F41zQJIYQQQgghxEmtqVPy0lRVnVf/9WxFUVYEKyEhhBBCCCFEZPHIGqZG5SuKMg1YB/QHDgcvJSGEEEIIIUQkOXW7S02fknctUIt3DVMNMCFYCQkhhBBCCCFEpGjSCJOqqg7gpSDnIoQQQgghhIhAp/KUvKaOMAkhhBBCCCHEKaepa5iEEEIIIYQQp6hIvu13sMkIkxBCCCGEEEIE8OcYYYrSof/b3SjxyahFBTg+8b+cSjdkDNrOfVCddhwfPA1uV4gT9U+r1zH8jamYMpKo2HWAZbe/7jdOidJyzpu38+11c46WDf3HJOKzW2Irreb7G59HdYe+f6/V6xj/2u3EtUyiZPcBFt35WpNjLn5uEsntM6gpq+LjSc/TfvDpXPTUjVQePALA5/e9SVluYUi359ich8311kv5rgOsmBq4Xoa/dTvfX/tbvQx5fhJx2S2xl1bzww3hqRd/dHod016fTkrLVPJ27+f5O+YEjNVGabl/7gwev34mAL3O6s1tT0+l5GAxAC/f+yKHcg+FJO9jOepc3LNgKcWVNXRsmcTsvwxBUZTjYtbvK+SVbzYCUFhpZfK5vWmZYPYpG3Nmx5DnfyxHnZt7/rWC4qpaOrZIYPalA323ZX8xr3y/FYDCyhomj+hBsjmGxxb/l4wEMwCPXtKftilxIc8/EEWvI+vV6egyUrDvyuPAXX72M62GNi/eiy49CUfuIQ7e92LoEw0kSof+8jt/O6d8+orfMN3gi9Cedqb3nPLhM+B2g06P/tJbcfzrHyFOunEOl5t7Fm+k2GKjY2ocs0ef4bu/FZTxysqfASistjF58GmMOb11ONL9n9S5XNzxwCxeefqxcKfSIIfDyZ0PPk5RyRE6ZbfjyYfu8amLqmoLtz8wC5fLzeD+Z3LzdVf4LQs7XTTmaY+hSUnDnZdLzfOPBwzVjxlPdJ8BWB6+m+ghw9GPHguANiMT69OP4NqxJVRZH0er1zH2talH2yj/udP3fN9QTN+J55M9vCcfXfl3ulw0gN7XjAQgqV0LFt36Egf+uztk2xJOqqxhimxRPYegVpdhf/leMJjQdujhE6MkpqGkZWJ/82HcezahxCWFIVP/si8dRE1hOYvOnUF0vIlWQ0/3idHG6Ljky1lkDPnt39L7dkKJ0vD5mEeJjjXQ6qzuoUz7qB5jB1FdWM7cUQ8QE2+i/RDfPPzFZPbphCZKy7yxj6A3G8ge6v2+DR98x/xxM5k/bmbYOksA7evrZfHIGejjTWQEqJcxv6uXtL6dULQallz0KDpz+OrFn7PHDqOssIw7zp+COd5MzyG9/MZF66OZs+R5eg7ueVz5V+9/wfTLpjH9smlh6SwBLNmUS3q8iY/vuIRqm4M1v/g+xaBvdkvm33IB82+5gI4tkuickey3LNyWbN1PepyRjyePptrmZM2+Ip+Yvu3SmX/DSObfMJKO6Ql0bpkIwPi+HY+WR1JnCSDxkmHUFZXyy6ipaOPNmP3sZ/HnDsC+az/7xk0jKi2JmK7twpCpf1E9BnvPKa9O855TsgOcU1JbY3/7Udx7NqPEJaMkpGKYNBtNi6wwZN24JTsPkR4bw8fXDqXaXseavFKfmL5tkpl/RQ7zr8ihY2osndMia99qiN3h4C/XT2HN+k3hTqVRn3/9A+mpKXz67qtUW6ysXrfRJ+aLb3+kQ7ssPnj9OTZt28nBw0V+y8JNf/ZIPGVHqL5jIorZTFTPPn7jNKnp6Ieff/S9c8UPWKZPwTJ9Cp6KMtx5+0KVso9uYwdhKSxn3qgZxMSbaDvE93wfKCauVTLdxw05Grfr87UsGDeLBeNmUVNSScmugpBthwifP0WHSdu+O+693iuw7tztaNr7adhmd0cxmIm54TG0WV1QK0pCnWZAGYO6cnjFdgAKV+2kZU5Xnxi3vY6FIx+gtqjiaJmttIodb38NgMcZvtGytjndyF25DYC81TtoO9A3f38xNaVV/HfeVwC469xHY7uM6svExTMZ//rtIcg+sJaDunJ4eeP1snjkA9QW/lYv9tIqdv5aL3WRMYr5q+45Pdi8wtuY2LpqK91zfBuCAE6Hk9vPm0JZ0fENqoGjcnjmszlMe3160HMNZN2+QgZ0yACgX3ZLNjTQqbY5XRwoq6ZTy6QGy8JlXW4xA7JbANCvXTob9hcHjLU5XRwot9CphbfD9P3OA1z5xlfc/dEKVDWyruqZcnpgWbEZAOvqLZgH+l40sCzbyJG3FoNWgzbOhMdSG+o0A9K2Px33Pu/nlXv/djTtfI99bfvTUQwmYq5/BG1WZ9SKEtTKI9hevjfU6TbZuoIyBmSlANCvTTIbDpQFjLXVuTlQUUunP1GHKUavZ+F7r5GemhLuVBq1buMWcvp6LyT0P/MM1m3c6jeuptaGqqqoqsruX/YFLAunqO69qdu8AQDX1k3ouvu/EGe8YQq29+f6lGvSW6LWWFFrrEHNsyFZOV3JW+k93+ev3kmWn3ZMoJhzHrmaH5/+2Cc+PjMVe3UtjurI+WwLNk8IXpGqwQ6ToigZgV4B4m9SFGWDoigb5m3Kbb4sjWZUe/0O6ahFMZh9f7cpDrWmGvtbj6DEJ6PJ6tx8v/8P0ieYcdYfUE6rDX2Cb/7+VO8vpnRzLlnn90ETHcWhH/1/4AabMcF89APBYbVhSDA1KaY8r5jDW3I57bw+aHVa9i3bSkV+MT8+9wlvX/ww5rQEsgZ0Cem2HCsm0YyzvhHntNjQJ55YvbQ5vw8aXfjqBWDS7Ft48t9PHX0lpSVRa6kBwGatxdzEfQ2gKL+IBc99wL1j7iIpLYnTB4Rn5Kyq1o45JhoAkz6aqlpHwNi1vxyiX4eMRsvCparW8du2xOiosjkDxq7dV0S/9t7OVeskM7cO78GCSedzxGJjQ17kXAACiEqMxVO/n3msNrQJsT4xnlo7qt1Bh38/jau0EueBwJ3FkDv2nGK3NXBOsWCf9xhKfFJEnVMCqbI5Met1AJj0UVTZ6gLGrs07Qr+s8I/Cnqwqq6oxm73nSpPRSLXF4hNzwbnDsFis3PHAbKJ1OhwOp9+ycNPExaHWeo931VaDYvbtZEcPPQd33j7cB/J9/k3XN4e6DWuDnmdDDAlm7Me0UWL8nBv9xXS9eCAluwoo+8V3xkXHc3qz74fNwU1cRIzG1jA9AlwNrAWOnXyrAsN/H6yq6lxgLkDNjPHNd0m0xoISY/R+rTei1vp+8KgOG5R6d2i1vDiipuTZKyxEx3nzj441YC/3zT+QNiN7023ieXx77XOonvBcZa6tsKCvz18fa6S2wjf/QDGdzulN/+vO46OJ3vxtlTXk1l/BqTxYiik5fFc37eUWomPr6yXuxOolc2Rvuk48j+/CWC8Abzx4/Hqyu164B2Os9yRtjDVhKa9u8s+yVlrYstL74V98sJj4lPjmS/QEJJhisNq9jQSr3UmCKSZg7PJdBxh+elajZeGSYNIfsy11JBj1AWOX/3yI4V29a0niDXoGZHvrMSPBRHmNPfjJngBXeTWa+v1ME2vE5Wc/0ybE4qm1sffS+2j/z8cxDexOzZptoU7Vv2PPKTEBzil2G5R5p4Oq5SUosYmhzPB/kmCIxurwdpKsDhcJRl3A2OX7ShjeqUWoUjvlJMbHYbV6OxnWmhoS4v2f62ZOv4OkxATuevBxkhITApaFk6e6CsXoPd4VownVUuUTo+szEE1qGrpefdG0aoN+9FgcXywEILpfDjVvhncNo63CQkx9GyUm1oDNTzvGX0z2iF7EZSTTbmgPkrNb0vuakWx891sAOpzTi28ffS90GxEBZA1TYDcDa1VVHa6q6rBjXj6dpWBy525D2+EMwDv1zp273SfGcygXTasOACjJLSJqSt7hlTtoVb9+J2NQNwpX72zS9xlS4+l+8wV8M+FZ6sLYYNq/agfZ9euW2uV0Jc9P/v5iTKnx5Ey6gH9e9yzO+vwH3DiKbhcNBEUhrVNrSvYcDN2G/E7hyh1H1x+1PMF6Of2WC/huwrO4Iqwhu2XVFnoO9U6X6JHTg21rmj76dfGNlzBkzFAURSHrtCwKfva9UhgK/bNbsqb+at66fYX0bd/Sb5yqqqzPLaJfdssGy8Kpf/sWR9ctrcstpm+7dL9xqqqyfn8x/dp5G7Dvr97NV9vy8XhU9pVU0SEt/I2mY1lXbSG2fj8z5/Sgxs9+lnrjJcSPHgweDx6bA40+OtRpBuTO3X503ZK2fTfc+3f4xHgKc9FkZAORd04JpH9WytF1S+sKSumb6X/qmqqqrC8oo18bGWEKlv59erJ6vXfd0n9/2kK/3mf4xPy0eTszn3kZp9PJz3v3c0a3zn7Lwq1uy0Z0PfsCENWjN3XbfNeQ1cyZhWX6FKzPzsS97+ejnSUMRjTJqXj8jDyFUv6qHbStb6O0yelGvp/zvb+Yz6e+yoJxs/hsyssUbdt/tLMUbTYQ2yKJMj9rbMXJqcEOk+oV0s6RP67NK1DikjBMeRZqrajlxUSff/VxMZ4De1BrLcTc8iSe0sN4Du4NU7a+9i1cjbFFImO/fQJHpZXq/BL6Pfi3Rr+v4/ghGNMTOG/BfVzw6UN0vHxoCLL1tW3RKmJbJDHpqyexVdZQUVDCyBlXNBizf9UOzrhsCOa0BK58fxrXfvIwPf9yFuvf/Zae44cycfFj7P56A6V+hrlD5dd6ubi+Xix5JfR9qPF66TB+CMa0BM798D5GLwxfvfizbNFSklsk88LXL2GpsrBl5RbSMtO5dsb1jX7vknf/w4jxI3nms+dY+/UaDvxyIAQZ+xrdK5uS6lrGP7+IeKOezORY5ixZ5xO3/WAp2ekJ6HVRDZaF0+gebb3b8soXxBujyUwyM+cr38Xf2w+VkZ0Wj16nBeCv/TuxeFMuV839mmFdWpOdFp7RvkAqF/+ILj2Zjl++iLvSiiO/iJYPHL+Plb63hKS/nEP2p8/grqjGsjxyFuq7tq70nlNufQpqa7znlPOuOi7Gc+AX7zll0uPec8qh8K8laczoLhmUWO2Mf2c58THRZCYYmbPUt2G4vaiK7BQz+ihtGLI8NVx47jCKj5QxdsItxMfFktmqJc+8/OZxMYMH9sHhdDLh1nu56Zq/YjQa/JaFm3PZt2iSU4h7YR6qxYKn8BCGa29p0vdG9+5/dP1TOO1YtJrYFolc/9UT2CutVBaUMGzG3xqMyV/leyHlV+3P6sH+FREyYh5Cp/IaJiVYi4mbdUpemH30buBpNH82h7QnTbWQ6VIaD/qTWKStDHcKzeajFwaHO4Xm4wy8furP5pdp4W+0NJfsCb7rKP+sNG0iY71dc4i65LZwpyD8sEy8LtwpNJu5mzPDnUKzuT//gz9dI+aatpcFvRH5bt6/I/Lv0uBl2EA3dwBQVVXGIYUQQgghhDgFeCLsjq2h1Kw3fRBCCCGEEEKIk0ljHaabgY6RsI5JCCGEEEIIER6n7vhSIx0m1bvASTpLQgghhBBCiFNSY7cVB0BRlOTfvR8fnHSEEEIIIYQQkcaDGvRXpGpShwlY+Lv3U5s7ESGEEEIIIYSINI3dJe8s4GygjaIoD9cXm4HyIOclhBBCCCGEiBBqBI8ABVtjN33IA34ELgOW1ZfZgMh5AqEQQgghhBD/HhShAAAgAElEQVQiqCL5wbLB1thNH/KBfEVR5qmquqyhWCGEEEIIIYQ42TQ2wvSrrxVFuR+I/rVAVdWZwUlJCCGEEEIIEUki+aYMwdbUDtMnwKNASfBSEUIIIYQQQojI0tQO0xFgoaqqrmAmI4QQQgghhIg8ctOHxm0AflQU5UPACqCq6nsNfoeuqT868p1MvUSHcvLs7ApKuFMQ/mhPnmMfjy3cGTQbjebkOfaJPon2sZPpeBERSYmSc6UQf1RTP6m31b9UkFaqEEIIIYQQp5JT+S55TX1w7YeAHugN6IAFQctICCGEEEIIISJEUztM7wAtgS+BVsD8YCUkhBBCCCGEiCyqqgb9FamaOiWvraqqV9V//bWiKCuDlZAQQgghhBBCRIqmdpgKFEWZAawBBgIFwUtJCCGEEEIIEUlO5ecwNXVK3s2AFhgHVAOTgpaREEIIIYQQQkSIpnaY3gMOApOBeLxrmoQQQgghhBCnAE8IXpGqqR2mNFVV56les4H0YCYlhBBCCCGEEJGgqWuY8hVFmQasA/oDh4OXkhBCCCGEECKSqLKGqVHXArV41zDVABOClZAQQgghhBBCRIomjTCpquoAXgpyLkIIIYQQQogIJHfJE0IIIYQQQgjho6lrmIQQQgghhBCnKFWVESYhhBBCCCGEEL8TuSNMUTr0f7kDJS4ZtbgAx8JX/YbpBl2EtlNvVKcdx0fPgtsNOj36sbfg+Pj5ECftn1avY+QbUzFnJFG26wBLb3/db5wmSsu5b97OV9fNabAsVKL0Ov726u3EZyRTtKuAT+56rUkxgb5vyKQL6TyiN85aOx/c+BzuOjc6g55xz93MP299IdSbh1av4+y5UzFlJFGx6wArpvqvFyVKy/C3buf7a711oGg1nPXqZAzpiVTvK2TV3W+GMu0G6fQ6pr0+nZSWqeTt3s/zdwTeb7RRWu6fO4PHr58JQK+zenPb01MpOVgMwMv3vsih3EMhyftYjjoX97z/HcWVNXRsmcTsv56NoijHxazfd5hXvtoAQGGFlcnn9+Hsrlnc+e63uNweBp3WmptG9g557r/nqHNzz8erKK6upWN6PLPHDvDdlv3FvPLDNgAKq2qYPLwHY3q2A+D9NT+zcs9h3rhmWMhz/z0lWkfmq9PRtUzFsXs/B+/2s29pNWS+cB9RaUk49x/i0DTvcd3qmTvRt2+Fq6yKglseB3eYn7ah1aEfNxUlLgm15ACOxf6Pfd3AC9B2rD+/fDzHe2659DYUvRH3/u3ULfskxIn7crjc3LNwPcXVNjqmxTH7wt6++1h+Ka+s2AVAYVUtk4d2YUz3NtS5Pdz96TpeHD8gHKn7cDic3Png4xSVHKFTdjuefOgen22pqrZw+wOzcLncDO5/Jjdfd4Xfsj+DOpeLOx6YxStPPxbuVBqmi8Z0z2NoUlJx5+dS++ITAUP1F45Dd+ZArI/djZKQhGnqAygGI84V3+H44tMQJu2fVq9j7GtTiWuZRMnuA/znTt9j319Mu7N6MOqpG6g6eASAL+97i/LcwlCnH3aR/JykYIvYEaaoHoNRq8uxv34/GExos7v7xCiJaSiprbG/8xjuvZtR4pJRElIx3DgbTYusMGTtX8dLB1FTWM4n585AH2+i9dDTfWK0MTou/XIWrYac3mBZKPW8ZBDVReW8PGo6hngTHYb41oG/GH9liZlppHVqzZt/mcmeH7cQ1yKZxNap3LJ4Fi27hqeu2l86iNrCcj4bOYPoeBMZAerloi9nkXFMHbQ5/0zKdxbw5SUzMaQlkNStTSjTbtDZY4dRVljGHedPwRxvpueQXn7jovXRzFnyPD0H9zyu/Kv3v2D6ZdOYftm0sHSWAJZs3Et6vImP77qMapuDNXt88+ibncH8yWOYP3kMHVsm0TkjmS827SU7PZF3bxvD5rxiDpVXhyH74y3Zmkd6nIGPbzmfalsda/YV+cT0bZfO/InnMH/iOXRMT6Bzi0QADlfW8Pnm/aFOOaCES4bhKipj3wVT0MSbMQ/23bfizh2Ifdd+9v/lPqJSE4np0g5jn64oUVpyL7sHrdmIeUj4O7JR3Qd5zy9vzoAYE9r2vse+kpDqPb+8Nwv3vi0ocUlE9Tob17ZV2Oc/SlTnvhBjDEP2x1uy4yDpsQY+njiMansda/KO+MT0zUph/lVDmH/VEDqmxdM5PR57nZsr5i9jrZ/4cPn86x9IT03h03dfpdpiZfW6jT4xX3z7Ix3aZfHB68+xadtODh4u8lsW6ewOB3+5fgpr1m8KdyqNih46ErXsCJa7b0AxxRJ1Rh+/cZrUdKKHnX/0vX70pTh++ALL9FuJHjEaYgyhSjmgbmMHYSksZ96oGcTEm2jrp30VKGbTB9+xYNwsFoybdUp2lsB7W/Fg/xepGuwwKYoSpyjKzfWv2GPK7w92Ytp23XDv8151de/fgaZtNz8xp6MYTMRc9zDaNp1RK0pQK49ge/XeYKd3QjIGdeXgiu0AHFq1k4ycrj4xbnsdn4x8gJqiigbLQql9Tjf2rvDWQe7qnbQf6Ju3vxh/ZdmDumGIM3HDvx4iq29nKg6UUHHwCC+ee1/oNuh3Wg7qyuHl3nopXLWTFgHq5bORD1BT+FsdHFq6lR1vfImi1RAdb8RpsYUs58Z0z+nB5hXeE/DWVVvpntPDb5zT4eT286ZQVlR6XPnAUTk889kcpr0+Pei5BrJu72EGdGwNQL/sDDbsC/zYN5vTxYGyajplJANQ63Ciqt4P3Z8PlYUk34as21/MgOwWAPRrl8aGvJKAsTaniwPlVjq1SADg6S83MmWE//oLB1NOD6wrvftWzZqtmAb65mZd9hOlby8CrQZtnBm3tRZXaSVl73wGgFpXF9KcA9G27Yp7v/fYd+ftRJPle+xr23VDiTERM+FBtJmnoVYewbXpR9w710JUNGg04HKFOnUf6/KPMKBtKgD9slLYkF8aMNZW5+JAhZVOafHE6LT838RhpMeGvxH7q3Ubt5DT19sR73/mGazbuNVvXE2tzXucqyq7f9kXsCySxej1LHzvNdJTU8KdSqN03XtRt9U7ou/avpGo0/1fiDNcPwXbgmNmXNTVoehjQKv1vo+A9S9ZOV3JW+k99vNX7yTLT7smUMxpo/oxYfFjjH19augSFhGjsRGmxXgfVNsLWKooSnp9+Wh/wYqi3KQoygZFUTbM+2nvH8vMYEZ11Hq/dthQDCbf32eKRa2pxv7OTJS4JDRtTvtjvzNIYhLMOKu921JntaFPMIc5o6YxJsZir+8MOKy1GPzk7S/GX5kpKY6a8mreunwW8S2TyOob/rrSJ5pxWurrxWJDn9i0enHVOnDbnYxe9DD2I1VYC8J3hXbS7Ft48t9PHX0lpSVRa6kBwGatxXwC+1pRfhELnvuAe8fcRVJaEqcP8B1RDIWqWjvmmGgATDHRVNU6Asau3XOQfh0yABjduwMWu5O73/uO6Cgtdpc7JPk2pMrmxKzXAWDS66iyOQPGrs0tol8770fsF1vz6NQigfapcSHJsym0CXG46/ctj6UWbbzvvuWptaPaHbT/v2dwlVZQd6AYZ95hbFv3EHvuQBSdDuty31GDkPM5v/hui2KMQ62txv7ebO/5JfM0cNSC6sEw+Tnc+7aCK3B9hkqVzYk5xju73qTXUWVvYB/bf4R+WamhSu2EVVZVYzZ7z/Umo5Fqi8Un5oJzh2GxWLnjgdlE63Q4HE6/ZaL5KLFxqLXeY1+trUVjjvWJ0Q0egTtvL54D+UfL7Es+IWbslcS9+B7O5d+Cwx6ynAMxJJix17fHHFYbMX7Okf5iKvOLWfHcJ7x38SOY0hJoM6BLSPOOFB7UoL8iVWMdJoOqqtepqjoJuA1YrChKwE9bVVXnqqraR1XVPtef2eGPZVZrQdHXT3fQG1FrfT84VYcNtcw7LKpWlKDEJf2x3xkk9goL0XHebYmONWAv992WSFRTbiGm/uqjPtZIrZ+8/cX4K3NYbZTWD2GXF5QQ1yL8dWUvtxAdW18vcU2vF32iGU10FF9c/BjR8SZa5ITvg/ONB187OoVu+mXTKC8uxxjrbXAYY01YTmBamrXSwpaVmwEoPlhMfEp8UHJuTIIpBmt9o89qd5JgigkYu3xXAUO7/DYl8pHxQ5lzzUh0UVqSTOG/cp5g1GN1eEdVrI46Eoz6gLHLfz7M0E7ezt/yPYdZl1vM/Z+sYWdhOR/9d09I8m2Iu6Iabf2+pYk14arw3be0CbEo0VHkjrsXbbwZU32nO3ZEP1KuHUP+jTPBEwGz4G3Hnl8MjZ9fKktQYhPB5O3A2l6+C22HM1ASwt/5SDDosdq9I11Wex0JhuiAscv3FjG0Q4tQpXbCEuPjsFq9DXNrTQ0J8f4vGMycfgcvPPkQ0dE6khITApaJ5uGprkIxeo99xWjCY6nyiYnuMxBd9zMx3fUw2vad0I8ai/HaydTOnUP1lKvRnTkQJSUt1Kn7sFVYiKlvj8XEGrBV+B77/mJslTVHR52qDpZiTI6ci1nCl6IoMYqi/EdRlC2Koryv/H4x5G9xiqIo7yqKslZRlM8URQl4b4fGOkxLFUVZpijKMFVV1wK34x11CvqiE/f+HUfXLWnbdcOdt9MnxnN4P5qM9gAoSS1QKwJPdwmnQyt30Hqod1syBnXj8GrfbYlEuau202God9pNdk43ctf45u0vxl/Zoe37adUjG4DktulUFIS/rgpX7iDjLG+9tBjUjaIm1ku3SaNpe2F/VI+Ky+ZEGxO4gRJqW1ZtoedQ73SJHjk92LbG/5QWfy6+8RKGjBmKoihknZZFwc/5jX9TEPTv0Io1ew4C3ul5fbMz/Mapqsr6vYePjjBtzC3i8X+vxOlys+dwGT2ywn9y7t8u/ei6pXX7i+nb1n9OqqqyPq/46AjT38flMH/iOfx93EC6tkzir/07hSznQKyrtxxdt2TO6UHNWt99K+WGscSNGgweDx6bAyVGT1RKAik3XUbexEfx1ETG9FX3/h1o29efX9p2w53v5/xSuB9Ny/rzS2I6amUJ+pFXoWndEdx13hsMRelCmrc//bNSWFM/1XNdQSl9s/xP8VJVlfUFpfQL8O+RoH+fnqxe7x2B/O9PW+jX+wyfmJ82b2fmMy/jdDr5ee9+zujW2W+ZaD6ubRvR1a9b0nXvhWv7Zp+YmudnY3lwCjVzZuLO3YPjy4UoBiOq0+m9SKKCogv/uTJ/1Q7a1q/HbpPTjXw/531/Mf1uHEWXiwaCopDaqTVH9hwIad6R4tdpr8F8NZOrgIOqqp4BJAIjA8QNAqJUVR0AxAHnBvqBDXaYVFWdDlwJ7Kx//19gGBD0RUKurStR4pIw3PIU2Kyo5cVEn3vlcTGeg7+g1lqIuXE2nrLDeA5F5rzlXxauxtQikXHfPoGj0kp1fgkDHvxbuNNq1ObFq4hLT2TKl3+nttJKeX4x5z9wRYMx+1Zt91t2YOMv1FZYuGXxLEr3FXJwS/jrKnfhaowtEhnz7RM4K61Y8kro81Dj9bJ7/rd0/OtQRn/2CI4KK4d/bHqnJNiWLVpKcotkXvj6JSxVFras3EJaZjrXzri+0e9d8u5/GDF+JM989hxrv17DgV/Cc0IY3bsDJdU1jH/u38Qb9WSmxDLn87U+cdsPHCG7RSJ6nfeC0KDOmThcbq579XNuHNELoz78jdnRPbIoqbYx/tUviTdEk5lkZs7Xvou8tx8qJzs1Hr1OG4Ysm6Zq8VKiWiTT4YuXcFdacOYX0WL68ftV2ftLSBw/kvafPIu70oJ1+UYSLhtBVGoibd+dSbuPnyJhfKDzVui4tq9GiU3EcOMTYLeiVpQQPeL4Y99zaC+qzULM9TPxlBXiOZyLc9VnRA//KzHXz8S1dzNqaeD1daEyultrSix2xr+9lPgYHZkJJub8sN0nbnthJdkpceijIncfu/DcYRQfKWPshFuIj4sls1VLnnn5+LuQDh7YB4fTyYRb7+Wma/6K0WjwWyaaj3P5dyhJqcTOeRuP1YKn6BCGCbc0+n32hR9ivPZWYv/+mne6XuHBEGTbsB2LVhPbIpHrv3oCe6WVyoIShs34W4Mx+at28NO739Jj/FCuWfwYe77eQNkv4T/2RYOGA9/Wf/0D3r6LP8XAr7dpbnAurxKsh1DVPPq3yJ2IeII+eCtyTzAnqiAqAqbDNJOOdRF7k8cTtlBbGe4Ums1HL58d7hSaT82fY/psU+yb8VO4U2g27a4/eaZcaVr5H0H9M4q66OZwpyD8sE5q/ILZn8UbG1qHO4Vmc3/+B36niUWyYa1HBr1tv/Tgt3/476IoytfAM6qqfqcoyg1A3/rlRYHix+KdRTdCVVW/C6AbfA6ToigBP8lVVZXutRBCCCGEEKJZKIpyE3DTMUVzVVWde4I/phT4dSF2fP37QL9vDN7O0kWBOkvQ+INrHwGuBtYCx/b4VLzDXUIIIYQQQoiTXCiek1TfOTrRDtLvfY93PdK/8fZX/uEvSFGUFniXGZ2vqmpNQz+wsQ7TzUBHVVWlcySEEEIIIYSIdAuASxVF2QpsAb5XFKUdMFlV1XuOibsGaAl8XX8jvXmqqs7z9wMb7DCp3gVO0lkSQgghhBDiFOaJgIcPN4Wqqg7gwt8V7wfu+V3cU8BTTfmZTVo1ryhK8u/ej2/K9wkhhBBCCCHEn1lTbzO28HfvpzZ3IkIIIYQQQojIpIbgFakau0veWcDZQBtFUR6uLzYD5UHOSwghhBBCCCHCrrGbPuQBPwKXAcvqy2yA75MXhRBCCCGEECclT0SPAQVXYzd9yAfyFUWZp6rqsoZihRBCCCGEEOJk09gI06++VhTlfiD61wJVVWcGJyUhhBBCCCFEJJERpsZ9AjwKlAQvFSGEEEIIIYSILE3tMB0BFqqq6gpmMkIIIYQQQojIo/5JnsMUDE3tMG0AflQU5UPACqCq6nsNfYO78OS5kV6xNiXcKTSb54pWhjuFZvNRwuBwp9BsknUx4U6h+RjN4c6g2eyavCLcKTSbdv2s4U6h2Si61HCn0Hw8nnBn0GwsE68LdwrNRolSwp1CszG/MS/cKTSfMx9uPEaIIGhqh2lb/UsFTp5PESGEEEIIIUSjTuU1TE19cO2HgB7oDeiABUHLSAghhBBCCBFR1BD8F6ma2mF6B2gJfAm0AuYHKyEhhBBCCCGEiBRNnZLXVlXVq+q//lpRlJNnIYwQQgghhBCiQXLTh8YVKIoyA1gDDAQKgpeSEEIIIYQQQkSGpk7JuxnQAuOAamBS0DISQgghhBBCRBQPatBfkaqpHab3gIPAZCAe75omIYQQQgghhDipNXVKXpqqqr/eyH+2oignz8NJhBBCCCGEEA2SNUyNy1cUZRqwDugPHA5eSkIIIYQQQggRGZo6Je9aoBbvGqYaYEKwEhJCCCGEEEJEllN5DVOTRphUVXUALwU5FyGEEEIIIYSIKE2dkieEEEIIIYQ4RakRPAIUbE2dkieEEEIIIYQQpxwZYRJCCCGEEEI0yCN3yYtAUToMkx5Ck5iK+1Au9nee8QnRduuD4eo78JQVA2B/7x94jhzGcMN0lPhkPMUHsb83J9SZe3PT6xj/2u3EtUyiZPcBFt35WpNjLn5uEsntM6gpq+LjSc8DcNlLtxGbnkhp7mE+v/fNkG6LP3q9nn/+83Vat27J9u27uf76OxqMnzJlIqNGjWD06CuIidGzYMFrpKensmXLDiZPvj9EWR9Po9fR763bMWQkU72zgJ+m+NYRgBKlpf+8O1k74VkAtEY9fV6dTHRSLOXr97Bj1j9DmbaPKL2Oya/eQ1JGMgd25fPWXf6XG/qLizbomfTiHZgTY9m7YTf/9/cPMMQZufmlOzHGmdi5YisL53wU0u1x1Lm4563/UFxhoWNGCrOvOR9FUY6LsTnqmP7OF1TU2OjZPoM7xw7F7nRx39v/obS6ls6ZqTx8xciQ5u2PotfR/vVp6DJSsO3KI/+O5/3GZc25nZjsVtSVVZF745NozQbavzkdJSqK6mUbKXrh4xBn7ocuGtM9j6FJScWdn0vti08EDNVfOA7dmQOxPnY3SkISpqkPoBiMOFd8h+OLT0OYdADaKPSXTEaJS8JTchDnEv+fqVH9RqHt0BPq7Dj+/SJ43ADoRlyBotPj/Cr8jyR0uNzcs2gDxRY7HVNjmX1BL5/jZX1BKa+s2A1AYbWNyUM6M+b0TOrcHu5etIEXL+sXjtR96aIxT3sMTUoa7rxcap5/PGCofsx4ovsMwPLw3UQPGY5+9FgAtBmZWJ9+BNeOLaHK2r+T6XhpgjqXizsemMUrTz8W7lT80up1jH1t6tH21n/ufP2EYvpOPJ/s4T356Mq/Y0pL4MI5k4g2Gdi5eDU/zf8mlJsiwiRip+Tp+o9ArSilZvYtKMZYtF16+41zLltC7TN3U/vM3XiKDxLVMwf3wVxqn7kLTXwSmtbtQ5y5V4+xg6guLGfuqAeIiTfRfkj3JsVk9umEJkrLvLGPoDcbyB7anc7n9aF4VwHvXPYYsWmJpHfNCsMWHe+KK8Zy6FAh/fqdT0JCPOecMyRgbJs2rbj66vFH348dO5rt23cxePBFjBgxmM6dO4YiZR+Zlw3CdricpSOmo0swkXqWbx1pYnSc/c3jpA49/WhZ60sHUf7TXlaMeYzY01pj7pgRyrR9DLxkKOVFZTwy6h5M8Wa6DTmjyXEDLxnCvk17eHLcg2R0yqRldiuGXn4OaxYu5/GxD3DmqP4Y40wh3Z4l63aRnmDm4weuptrmYM3uAt+Y9bvo3q4l7979V/YVlpNbVMb3m3+hY6tUPpx2BWt3F7CvsCykefuTNPZsnIVl7D7vDqLizcQO7ekTY+rbBSVKy88X34fWbCBuaC8SLzkL+54D7Ln0fkx9uhCdmRaG7I8XPXQkatkRLHffgGKKJeqMPn7jNKnpRA87/+h7/ehLcfzwBZbptxI9YjTEGEKVckDabjl4LBXY33kEJcaIpm03nxglPhVNSiscHz6JO3cbSmwSAJqW7dC29/2sCJclOw6SHmvg4+vOotpex5q8Iz4xfdukMP/Kwcy/cjAdU+PonBaPvc7NFe8tZ62f+HDRnz0ST9kRqu+YiGI2E9Uz8D6mH/7bPuZc8QOW6VOwTJ+Cp6IMd96+UKUc0Ml0vDTG7nDwl+unsGb9pnCnElC3sYOwFJYzb9QMYuJNtB1yepNj4lol033cb22cM685l60fL+f9sY9yxuVnoTPqQ7Yd4aaG4L9I1WCHSVEUnaIoYxRFGap4XaMoys2KohiDnVhU5564dm0EwP3zZqJO898I1PUehOn+FzFMeggA144NOL/9FDQaMJpQ7bXBTtWvtjndyF25DYC81TtoO7Brk2JqSqv477yvAHDXea9m7v1xC2ve/AJFqyEmzojDEp5tOtbZZ+fw/ffe5xf/+OMqzjorJ2Dss88+ykMPPXX0/e7de1mwwHvVzOmsC26iDUgZ3I0jy71//yMrd5I6yLeOPPY6lg6/H1th+W9lzjqiDN4PSK1eh8fpCk3CAXTJOZ2dK7xXU3et3kbngb6Nv0Bxdc469DHebdHpo3HVuVj+r+9Yv2QN0THRaDQaXCGuo3V7DjCgs/eiQL9OmWzYc8AnJjpKi93pQlVVnHUudFot7VokcWG/Lt5t0WpDmnMgsYO6Y1mxGQDL6q3EDvRtaLuOVFIy73MA1Lrf9iWNydtQUhQFQ7fwXPg5lq57L+q2bgDAtX0jUaf38htnuH4KtgXHjNjU1aHoY+DXOomAKR3arC548nYA4C7YhbZNF58YTdsuEGNEf8X9aFp3Qq06AhoturPGU7cicq76rysoY0DbFAD6ZaWwoSDwhQJbnYsDFTV0SosjRqfl/647m/TYmFCl2qio7r2p21y/j23dhK67/33MeMMUbO/P9SnXpLdErbGi1liDmmdTnEzHS2Ni9HoWvvca6akp4U4loKycruSt3A5A/uqdZPlpkwWKOeeRq/nx6d9G+d3OOnSGaDRRv9ZRkJMXEaGxKXkfA4VACvAcsBuw1Jdf+PtgRVFuAm4CeH5IV67r0vp/TkwxxaLaagBQbbVo0n1/lnrkMI7F7+Havg7jff9A26kH7j1bATDd/wKeqnLU0qL/OYc/wphgxlHt7dg4rDaS27dsUkx5nnd64Wnn9UGr07Jv2VZUj/donLjoMSwllVQeCP0VwRdemE337r81Kurq6qiutgBgsVjp1Cnb7/ddfvnFbNu2i927fzlatmmTt5Ny220T2bRp23H/FkrRibHUVdsAcFlqMWf71pE/Bz9dzdAlj5FxUX+OrNxObX5JMNP0cdWsG8js/Nsoo8vlxlbfibZZbbTI9j/iZU6M9Yn77+KVzPj0CfpcMJBdq7ZxpMC7/ykaDc+sfJX1X6zBaXcGeYuOV1Vjx2yIBsAUE01ecYVPzKi+nbnm2Y/4dtMe+p3WhszUhKP/9sEPG+nSJo3slskhyzmQqMQ43PV/c7fFhr59K58YR14hAPHnD0DRRVG9bJN3pOmsXrSfez8eZx2amOiQ5u2PEhuHWlv/mVxbizYj0ydGN3gE7ry9eA7kHy2zL/mEuKfnEnPplTi++Qwc9pDlHIhiMKM6vMe+6rCjJPke+4ohFmwWHJ++iP6qGWhad0TTuhOuHatRa6pDnXJAVTYnZr0OAFN0FHnlgTsLa/NK6ZcVuY1aTdwx+5itBk0r330seug5uPP24T5mH/uVrm8OdRvWBj3PpjiZjpeTgSHBjP2Y9laSnzaZv5iuFw+kZFcBZb8cOhq3Yd5XXPuf2QycPIZNC36gzuYIzUZEAFnDFFiKqqpjARRF+UhV1avrv17pL1hV1bnAXIDqSef9ob+qaq1GMXinAikGE6rV9wSl1lhw7fYOAatlxSixCd6OlsNOzVN3YrzrKbSdzsC9J/RzmWsrLOjjvANx+lgjtRWWJsd0Oqc3/a87j48mPofqUTEkmHHW2pl36c1OlcEAAB/1SURBVKNM+OcM2g7sSt6anaHbGOD22x887v38+S8QFxcLQFxcLGVl5f6+jdGjR5CZ2YpzzhlKp07Z3HzzNbz++rvceONVDBrUl6uvvi3ouQfiLLegi/NexdfFGXGW+9aRP52mjiHv3e/I//BH+rx2G0l9OlK+IXSdvg8eeuu49zc+fzuGWO9+ZIw1Yg2wHZZyi0/c6FvHsnTBN6z41/dMevEOsnufxpGCIizlFu4bOplZX83hu3lLOHIgdJ3CBJMBq83bSbPanCSYfaekzPt6PeMG9+DSQd25f94XbM49TM/2GXy8fAub9h3i79ePDlm+DXGVV6Ot/5tr44y4/XwOAMSP7Efa9Rey77rHweMBoOCel3CVV9PutftwlVaFLOdAPNVVKMb6z2SjCY/FN6foPgPRpKSj69kPTUYm+lFj0bbrSO3cObi2b8L86Bycq5ailob2IsPvqbUWFH39CJ7eALV+6sVpx1PmveCmVh5BiU30TsVTVRS9EcUUh/a0Prh/3hDK1H0kGKKxOryjwFaHiwRD4M718r3FDO/UIlSpnbDf72Oqn31M12cgmtQ0dL36omnVBv3osTi+WAhAdL8cat58MaQ5B3IyHS8nA1uFhZj69lZMrAGbn89ifzHZI3oRl5FMu6E9SM5uSe9rRtLi9LZ89cA8Ctbs5G//fIDdn6+l+nD4p4CL4GpsDdM2RVHmKopiUlX1r4qiGBVFmQFUBjsx1+5NRHX1rlvSdj4D18++nZ7ocy5D1+csUBQ0GVl4DucRPXIcujOHgOoBpwMlOjxXZvev2kF2/bqldjldyVvt28HxF2NKjSdn0gX887pncdZ4rywNvHE0XS/oj+pRqbM7iYrRhW5DAli6dBXnnDMU8E7PW7Zsjd+4a66ZyvDhlzFhwm1s2rSN119/l+7duzBq1AiuvPJWXK7wTWc7snI7qWf1ALzT80pXNa0TGmU24K5voHicdWhN4Z3SsmvVNroN9U5Z7ZxzOrvXbG9yXIzJQJ3D2zmpc9YRY4rhrw9dS4fenXA56nC7XOj0oT2G+p+WyZpd3iuu6/YcoG8n39HlGrsTvc57vUcXpaXWUcfPB4+wYsd+np54QcRMybOs2nJ03VJsTg8sq7f5xESlJpB+8yXsu2YWnhrvqIe5fzcyn7wFJToKQ9e21Gz8OaR5++PathFd/ToMXfdeuLZv9ompeX42lgenUDNnJu7cPTi+XIhiMKI6nd6OoAqKLvyjZe78XWjaedcnaLO64C7Y7RPjKcpD07IdAEpiGmrlERwf/h3HP5/C+f2HuPdtDXtnCaB/VsrRdUvrCkrp28b/CJKqqqw/ENkjTHVbNqLr2ReAqB69qdvmuyamZs4sLNOnYH12Ju59Px/tLGEwoklOPW60JpxOpuPlZJC/agdt69tbbXK6ke+nTeYv5vOpr7Jg3Cw+m/IyRdv2s/Hdb4k2xeB21Hln/6jeqfmnClnDFICqqrcCbwC/tmqjgBLg8iDnRd26pSgJKZgeeg21xoLnyGH0l914XIxz6Wfocs7FdP8LuDavxlNYgPPHz9DlnPf/7d15fFTV/f/x1wFCCBAICfsaguwIgigQZUctaqvIYkVAxLq0fqXa+nWBCgK2qD+xWi3660MRUbBVFFD7A1xQhABWrQoGEGRJQJaAIZCQsOb8/phJDMlMMoG5d27C++kjD2eun4yfT849c+bMnHuGmg/+lfyjRziV+pXTqQa0YXEKsY3juXPZTPKyjnIoPYMrJo8uNWZHSirdhveldsM4bn7tQcYvnMJFo/rzxbwPuWhUfyYsepTcQ9lsW7k+IjUV9cYbi2natDFffLGcQ4cOs2LFahITWzBz5uQyf/f228fQqlVzPvroLVaseJsrrxzgfMIB7H47hZgm9Ri44nFOHsrhaNp+Ok8dXebv7XjlA1rfMoR+70+jSo3qHFgVeILilnVLPqNeowSmLZ3F0awcNqZsoH7zhoyaNK7MuBWvLWPgmKuY9M6fqV6jOhtTNvDv2YsY8dAYprz7BN+u+C97ftjtaj1XX9KBjMM5jPzza9StGU2L+nE8/c5nZ8Tc2L8bb676lnFP/ZPjJ0/Rq30LFq5az56fjnDbX99i/Kx/sTp1h6t5B5K5aCXVGyfQ8YNnOZWVzfG0vTT70/gzYhJGDKJaw3gueP1R2r09k4QbB3P4k6+oEl2ddm/PZN/f3iI/N/LLck589hEmvgGxT79Mfk42+ft+JGbcb8v8vWOLFlBz/O+IffwF3/Kjve6eT4Gc3riWKrXjqHHrdGzeUWxWBlEDzxzW8vdsw+ZlEz1uCjZzH/l7I38+BXJ1p2ZkZB9j5CufUrdGFC3iavH0J6kl4r7bl0WbhFiiq3njzYRATqz8kCoJ9anz7Bxsdjb5e38kZnzZ5xhA9R69Cq9/8oLK1F8qg9TFa4htXI8Jy/7CsawcstIzGDj5plJj0lJK9iOAtbPfY9AjNzNuyTT2b0zj0I7IXPoh7jLWofWI57okz0ueWebdd+TK6y/7V0U6hbD5Z9zlkU4hbN6t4e51Qk76+0tDIp1C2Gy6dWmkUwib1pdGfllfuERf2jrSKYSNqR/5a+3CJe/dLyKdQtiYaqbsoAqi9v+dE+kUwmbWxVMinULYPJT2eoU7ydo16On4a/stB7705N+l1GuYjDFB90u21u4JfzoiIiIiIiLeUdamD1OBscA6oOiMzwKDnEpKRERERES8w8vXGDmtrAnTXUBba60mRyIiIiIi5yltKx6E9V3gpMmSiIiIiIicl8raVhwAY0xCsfsjnUlHRERERES8RtuKl21RsfsTw52IiIiIiIiI15S1S15/YADQ0hhTsJdjbSDT4bxERERERMQjrM2PdAoRU9amDzuBT4HhwEr/sTyg5Ndvi4iIiIiIVDJlbfqQBqQZY+ZYa1eWFisiIiIiIpVTvoevMXJaWZ8wFVhujHkIqF5wwFo73ZmUREREREREvCHUCdNC4FEgw7lURERERETEi6y+h6lMB4BF1tpTTiYjIiIiIiLiJSaU2aIx5imgN7AAyAGw1s4r7XeebDWm0kxD083JSKcQNqHuI18RxIU83/e+XCrPzjMJtmqkUwib45VovXY0JtIphE3NytMs5FaeZqlEZ5h41R+/qjxXg0TVT6pwXaZ5fBfHn313Z37nyb9LqK84N/h/LHpOFBERERGR80SoHzgsAKKBHkAUMN+xjERERERExFOstY7/eFWoE6ZXgCbAUqAZMNephERERERERLwi1CV5idbaMf7by40xq51KSEREREREvCXfw58AOS3UCVO6MWYysBboA6Q7l5KIiIiIiIg3hLok7y6gKjACOALc6VhGIiIiIiLiKdaFf7wq1AnTPGA3cDdQF981TSIiIiIiIpVaqEvyGlpr5/hvP2aMWeVUQiIiIiIi4i1e3sXOaaFOmNKMMQ8C/wF6AXucS0lERERERMQbQl2SNx7IxXcN01FgnFMJiYiIiIiIt+RjHf/xqpA+YbLWHgeeczgXERERERHxoPN5SV6onzCJiIiIiIicd0K9hklERERERM5T+uJaj6gaHcX1L0wktkk8Bzbv4t/3vRhSTOv+XfnFE7/h8O4DACx74CVqNYyj7/0jAKjTrD6rnlpI6tvub+5XLTqK38y+j7im9dmzKY15f/h7ueMG3nY1nQf24PkxjwEwbPJYki5ux/7te3j9/hdcqSNQvhNm30c9f76vlVJXsLgB/rr+7q/LLdWio7hx9u+p2zSefZt28c4fSv4Ng8VUqVaVm168l/m/mQVAYu+ODP7jSADimtXn41lv8Y3L51m16CjGzb6XuKYJ7N2Uzht/mF2uuF/P+i0NkpqQc/AIr971NPmn8wMec1rV6ChuKNK33wvS/4PFXHLbL2gz6CL+efPjVIuO4vrZ91C7QRz7Uney7OE5JR7LSdWioxj1wu+p0ySe/Zt3sei+wOdYoJjrZ91J/aSmHP3pMP+68xnyT+cHPOaWcLZLgSFTxxJVM5qlD77kSg1F87zmxYnUbhLPwc27+ODewLUUj2nWuyPJ/+sbT2Kb1WftUwvZtNDXz7vf9gsSB1/EotGPl3gsp2upLP2lanQUw16YSJ0m8WRs3sX7QWoJFlO0lo6/7E2PW64AIL51Yxb/7jl2fb7ZtVpCybe0mNb9uzK0yOuZpQ+8ROb2vW6nHzS/8sQUbZdaDeO49uk7qV4rho1L1vDV3A/cLKVcTp46xb2TZvD3J6dFOhXxEE8tyes87DKy92Yyd+hkatStRWLfLiHHfP36RywYMYMFI2aQuX0vu9ZtKrx/YPMuMlJ3ulyNzyXX9yVrXyaPD32AmnVr0aFv13LF1WtWn14jBhTGte3TmRO5x5h1wyNk7j5ATJ2abpQRNN8nhj5ATAh1FY8rXpebul5/GUf2ZTJ76CRi6taiTd8LQ4qpFh3FXe89RlKR83Lnuk28PHI6L4+czv7N6eyNwHnW4/rLObwvk6eHPkRM3Vq0C1BPsLjEnu2pUrUKzw2bQo3YGNr17RrwmBu6DLuMI3szmePv260D9P9gMXWaJXDhiL6Fce2vvpSMzbuY+6sptO7bhYS2TV2poUBXf54vlnaOBYhp2bMdVapV5aVhU4muHUObfoGPuSmc7QLQpFsSbQa4c04V12HYZeTszWTBL3x5tgxQS6CYH9dt4q3hM3hr+AwObtrFge92AhDbLIGOI/uWeAw3VKb+UjCuzwlh7C8eU7yWTe+tY/6IGcwfMYOjGVlkbEp3rY5Q8g0l5uvXPyqsIVKTpdLyCyWmeLtcfMuVrH/zM14b9ijdbuxPVM1o1+ooj2PHjzNqwj2s/eLrSKfiSdZax3+8ylMTppbJndi5+jsA0tZspGWfTiHHtB96KWOXTOO6FyeeEV+tRnXqtWrEgc27HM4+sPbJndm8aj0AW9ak0q5P53LFjZg6nnefWFAY1+GyLjRs05T7Fz9GTJ1a5B3JdbiCwNomd+Z7f75b16TSNkhdweKGTx3Pe0XqclNScme2rdoAwI41qbQOcJ4Fijl1/CSzhz7Mkb2ZJeKjalQnvlUj9kfgPLsguTNb/Ln+sCaVNkHaIlBczsHDrHplGQCnTpwCCHjMDa2K9O2dQfp/sJgrpo5l5ZNvFsb9tPVHvntnNQCnT5x2OvUSWid3Ztvqn8+fxAC1BIrJOXiYz+f4/vanT/ryDnTMTeFslyrVqjLw4V+z8qmFLmReUovkTqSv8uW5K2UjzZNL1lJaTLUa1YlLbMRBfz/v/+hY1jzxZonHcENl6i+tio3rrcqopWjMkKlj+fTJkm1Qt0UDjh3J5XiExshzqan90EsZt2Qaw4q9nnFbONvl9ImTRMVUp0q1qr4DHn1dXCM6mkXzXqBRg/qRTkU8plxL8owx0621U5xKJiauduGT24mcPOKTmoQUk5W2n1WzFrJ9xTfc/M4UWvTuyK51mwBI7NuFtDWpTqVcwqgZt9GsQ8vC+6dPnSYvOw+AvJw8GrYJ/M5drXqxJeJ6/uoyftyUxr4ffiyMqx1fh73f72LuxL8xbdXzrHjpfQ7t+cnBinxGzriNpkXqyi9S17EQ6yqIuzhAXW6qWa82x7JzC3NKaFPyPAslpqg2fS9ku0vn2Q0zJtCkQ4vC+6dPnT4j1wZBcq1VrKYGbZpwcOc+ALpc1ZNq1avx/WffYvNtiWNuKN63E0Lo/wlJTeh0XR8yNqVzcOvP59M+/ycAl0y4in3f7eCnre5+dVzRPI+HUEtBTObO/QB0uKonVaOq8sPK9YXtUfSYm8LZLr3uvIYNb68m96cj7iRfTI16tTme/XOe9QL0ldJiWvbtwq4UXz9vf10fDm5K56etkXkeq2z95ViRvhBs7C8eU1BLoDZoO6QH21Z842zipTjbmgpez2xb8Q1j3plCy94dSfe/nnFbONvlyznLGP/+Y/S5+1d8PX8FJ/OOu1OEhJWXt/0uyhhTA1gItADWA+NsKR9fGWPuA66x1g4JFlPqhMkYs5Wf3wcwQFNjzE2Atda2CxB/B3AHwA3xl9KrdtvSKyom71A20f4lZtGxMeQdyg4pJi/rKEf873Ac3n2Qmgl1CuMvGNKDLcu+KFce5+LNR14+4/4tz9xDTGwMADGxNTmaGfiFQk5mdom4LoN7UK9pfTr260ajpCb0G3cVx3Ly2L99DzbfcmjvT9RtFO/KhOmtYnWNK1JXjdia5ASp62iRugriOhepq2FSE/qOu4pV85Y7W0CxnGrE1izMKTez5HkWSkxR7Qd3Z6NL59k7j5x5fcHoZ+4uzNV37gTOtWhNReM6DbmYvrcOZc5t/6fwxXmgY04r3rdzQ+j/uYeyuWBwd+o0TaB1v67Et2nCxbdcwVevfkj3MYNpfml7lvxP4OvrnJR7Rp41A9YSLKb9kB70vvUqFtw2q/BvH+iYW8LZLkn9u2LzLTXq1KRWg7q0v/oSvv9/7j0/52VmE+3vA9VjY8gL0FdKi2k9pAfb/P289eDuxDZLoGW/rtRr04Sut1zB+lc/dKEKf56VqL/kHcqmRp2C59vgY3/xmDZFaklo04Qet1zBf/1tcMGQ7nz46Dz3iggh31Bi8rKOFn5iU/z1jNvC2S6NuySybNIc0tdu5KY3JrH5vXUcceG1i5y3xgC7rbXXGmPeB64AAl44Z4xphe/7Zg+U9oBlLcl7ANgL3GWtbQv8x1rbNtBkCcBa+w9rbU9rbc/yTpYA0lJSSfSv9W+Z3Jn0NRtDirnk9qF0+GUfMIb67ZpzcMvPy6Ja9OlIWoDHccv3KRvo0K8bAO2SO7NlbeBcAsXN/f1z/HXkVF6551nSN+zgs3nLSf9uO626tsFUMcQ3TSBzd6nt65gtxfLdGqSuQHHzfv8cz46cytx7nmXXhh2uTpYAtqekFl4HkpTciR0Bcg8lpqjWfcqOccrWlFTa+XO9ILkz24LkESgutkFdBtxxLS/f+iTHjx4DCHjMDTtTUmnt79utgvT/QDHvTpzN6yNmsOSe59m3YQdfvfohDTu25IJBF7H4d8+Rf8r9JUY7UlILr1tqndyJnQFqCRRTu0Fdku+8hvm3PsUJ/98+0DE3hbNd5o96jAW//jMfTX+dH1Z84+pkCWBXSiot/X2gRXJndgfoK6XFNE/uyC5//csmzuat4TNY+j/Pk7Fhh6uTJahc/aX4uB5ozA4U897E2cwfMYN3/bUUTJaq144htnG865+UlZVvKDGX3j6Ujv7XMw3aNefAlshcThAsv1BiArVL9Vo1OH38pO8NH+vbLEIqngp0DdMgoOBJeQUwsJTYZ4GHy3rAUidM1tpFwLXAcGPMy4CjV+ltXLyG2Mb1GL/sLxzLyiErPYMBk28qNSYtJZX/vvohF47sx9gl09i6/MvCJ8nG3ZL4acuPnD5+0sm0S/XlktXENarHw0ufJDcrh+9TNpDQvAHDJo0pMy6Qb5Z+TnyzBjzw7kz+s2gVRw5kuVFGCV8uWU3dRvV40J/vlpQNxDdvwHUB6ioeF2nrl6RQp1E8v1s6k9yso2SmZXDVpNGlxmxPCb7crlm3JDK27OZUhM6z/y5ZTd1G8fxh6RPkZuWwNeU74ps34NpJN5cZ13N4P+o0jOP2eQ9x91tTuWTkgIDH3JDq79u3+fv2ofQMBhXr/8VjdgZpl+43D6Ju8/rc/OafGLPwEZJc3mRg/eIU6jSO57fLZpKXdZTM9AyunDy61JjtKal0G96X2IZxjH3tQSYsnEL3Uf0DHnNTONsl0r5fvIbajetx8/K/cOxwDofTMri8WC3FY3at9tXSqFsSmREeT4qqTP2lIM8JRcb+gUFqmVBk7A8mqX9XdqyK7FhztjV99eqHdB3Zj1uWTGNLkdczkRDOdlk7+z0GPXIz45ZMY//GNA7t2OdGCVIBGWPuMMZ8WeTnjrN4mATgsP/2ESA+yP9rNPAtUOY73ibU2ZwxJhm4zlr7YCjxT7YaUzEWOoYg3XhjgAwHT+3ycY7ivLUr/jnJxb1top2WYKtGOoWwOV5B1muHIhoT6RTCpmblaRZyK0+zVKIzTLzqj19Nj3QKYRNVP6nCdZnaNVs7/uybk7vjnP8uxpj5wDvW2reNMX8E4q21kwPELQBa4rtEqT3wiLX2+UCPGfLrZ2vtmlAnSyIiIiIiIhHwMXCl//Yg4JNAQdba0dbay4FfA18FmyxB2Zs+BP0yBmtt5D4nFhERERER19iKs+piPnCDMWY9viV3HxtjWgN3W2vvP5sHLGtN01RgLLCOMz9tt/hmbCIiIiIiIp5grT2Obw+GonYAASdL1tqdQNAtxaHsCdNdQFtrrSZHIiIiIiLnqfzw7WJX4ZQ6YfJ/yZMmSyIiIiIicl4KadMHY0xCsfsjnUlHRERERES8pgJ9D1PYhbpL3qJi9yeGOxERERERERGvKWuXvP7AAKClMWaK/3BtINPhvERERERExCMq0C55YVfWpg87gU+B4cBK/7E84GvnUhIREREREfGGsjZ9SAPSjDFzrLUrS4sVEREREZHKycvXGDmtrE+YCiw3xjwEVC84YK2d7kxKIiIiIiLiJZowlW0h8CiQ4VwqIiIiIiIi3hLqhOkAsMhae8rJZERERERExHvO38+XQp8wfQl8aoxZAOQAWGvnOZaViIiIiIiIB5hQ1iMaY27x37SAAbDWvupgXiEzxtxhrf1HpPMIB9XiTarFm1SLN6kWb1It3qRavKky1SLhEeoX1y4AooEeQBQw37GMyu+OSCcQRqrFm1SLN6kWb1It3qRavEm1eFNlqkXCINQJ0ytAE2Ap0AyY61RCIiIiIiIiXhHqNUyJ1tox/tvLjTGrnUpIRERERETEK0KdMKUbYyYDa4E+QLpzKZVbZVpjqlq8SbV4k2rxJtXiTarFm1SLN1WmWiQMQt30oQ5wL9AY2ATMtdZmO5ybiIiIiIhIRIV6DdM8YDdwN1AX3zVNIiIiIiIilVqoE6aG1to51ucxoJGTSZ0tY0yiMWZAsWPGGPMPY0zzCKV1VoLUMtMY87kxZqUxJi5CqZVbkFpeMMasNsYsMcZERyi1cgtUi//4MGPMDxFI6awVr8UY08EYc8DfLqsD1elVQc6xMf6+kmKMiY9QauUWoF3aF2mTrcaYqRFMr1wC1FLFGPOqMWaVMWaeMSbUMSjiAtQSZYx5139+zY5gaiEJZXz033/FGLPeGLPUGBMTkWTLEOpYXxHG/3LU4vnxvxy1eH78L8/ryYo4/kv5hTpYpRljHjTGDDTGPATscTKpc5AIDCh2bAUwzvVMzl0iRWoxxrQALrfW9gJ2AKMjk9ZZSeTMWnoCUdbay/GdS0Mjk9ZZSaTYOWZ8S1bvi0Qy5yiRkv1lsbX2cv/Pp65ndPYSOfMcqw2Mttb2B14AkiKT1llJpEgt1trvC9oE+AD4OkJ5nY1EzjzHhgDHrLV9gWzgqgjkdLYSObOWK4Gq1trLgKoV4A2GRMoeHwcDTay1XYFtwFhXMiu/REIb6yvC+J9IGbVUoPE/kbJrqSjjfyIhnGMVePyXcgp104fx+PakHwFsxsEnIGNMDXzblrcCDuB7wn4RaAmk+XMZDVwI9AQa+vO6EpgA1DXGDAGGWWsPWGsHGmM+dSrf0oSzFuAk8Kj/oU+4VEKhMNeSys9PMPWBLJfKAMJ/jgGPA9P9j+GqMLcLQLIxZg2+c+waa+3RClrLRUCUMeZjfG8MXeNWHeDIOVZgCPC/rhThF+Z2OQ7U9D90beCYS2UAjvSX2IKHBroDn1aU/IOMj4OAD/23VwAjcejidzfGerfGfxdqcW38d6EW18Z/l15PRmz8F3eF9AmTtfa4tfY5a+3d/n8fdzCnO4BvrbV9gHfxbTax0f8u3lbgVn9cH+AKfE8i11lr/wrcA8zxvxt7oMQjuy9stVhr91lrPzbG9MbXsd3+8uBw1pJnrc02xvwL3zv/qypqLcaYy4AYa+1HLtdQIJz95SAw01qbjO+dv5GuVhLeWhrg29RmMLAPGO5qJQ48jxljugHbrLW57pUBhLfvrwTijDEbgQRr7ScVtRZgOb7VF/8GagFuLF9zenxMAA77bx8BnFzKqrE+xFpcHv+drsXN8d/RWjww/ouLvLh+vAPwH//tOfh25lvrv78W6OS/vcBaewLYD1R3NcPQhbUWY0wv4G/Atdbaw8HiHBK2WowxdY0xtay1N+LbddHtJRPhbJfrgI7+d52aGWMecyTj4MJZy0lgkf/2TnzvtrkpnLUcAbb4b2/H94XbbnLieeyXwPthzjMU4ez74/C9gOkEfGeMcXtpUTjbJRp40Fp7Db43GDIcyfhMTo+PB/Ft7IT/3wfPKdvSaawvBxfHf0drcXn8d7pdIj3+i4u8OGHaDPTy357sv9/bf783vo9zAXIC/G4evnf6MMYYB3MMVdhq8X+0/AzwS2ttJK4hC2e73MjPH8ln4847s0WFrRZr7QPW2t7W2gHAj9baPzmWdWDhbJdRwP3+/9YN2OBAvqUJZy3fAN39ty/AN2lykxPPY5GaMIWzllh+XoZ3DN+yPDeFs5Y2wHv+/zYA3xI2pzk9Pn6MbzkS+JbnOfkJoMZ6nzJrcXn8d7pd3Bz/Ha3FA+O/uMiLE6Z/ABcZY1bjWxM+F+hsjEkB2vnvB/M10N4fO8rhPEMRzlquA5oDbxvf7jKTnEw8gHDWsgDo47/fCt+29W7SOeYTqF16GWPWAXuBZQ7mHUjYarHW7gb+DXwO1AGWOJh3IGE9x4wxjYBoa20kvjQ8nLXMBwb47ycDbziYdyDhPMfWA+uNMV8Cb1tr3ZiUO/3c9QnwozFmA77lUq+HKe9A9DzsE0otbo7/Ttfi5vhfmc4xibCQvrhWRERERETkfOTFT5hEREREREQ8QRMmERERERGRIDRhEhERERERCUITJhERERERkSA0YRIREREREQlCEyYREREREZEgNGESEREREREJ4v8DH+ifBznG/RAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.07 s\n"
     ]
    }
   ],
   "source": [
    "# 得到各个变量的相关性\n",
    "plt.subplots(figsize=(16,9))\n",
    "correlation_mat = train[cont_features].corr()\n",
    "sns.heatmap(correlation_mat , annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cont4    -0.035831\n",
       "cont5    -0.011355\n",
       "cont1    -0.010237\n",
       "cont13   -0.004022\n",
       "id       -0.001122\n",
       "cont9     0.014456\n",
       "cont14    0.019298\n",
       "cont10    0.020236\n",
       "cont8     0.030508\n",
       "cont6     0.040967\n",
       "cont12    0.098724\n",
       "cont11    0.099806\n",
       "cont3     0.111053\n",
       "cont7     0.119799\n",
       "cont2     0.141528\n",
       "loss      1.000000\n",
       "Name: loss, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 134 ms\n"
     ]
    }
   ],
   "source": [
    "train.corr()['loss'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 248 µs\n"
     ]
    }
   ],
   "source": [
    "# for feature in cat_features:\n",
    "#     sns.countplot(x = train[feature], data = train)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 8.37 s\n"
     ]
    }
   ],
   "source": [
    "for c in range(len(cat_features)):\n",
    "    train[cat_features[c]] = train[cat_features[c]].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>cat10</th>\n",
       "      <th>cat11</th>\n",
       "      <th>cat12</th>\n",
       "      <th>cat13</th>\n",
       "      <th>cat14</th>\n",
       "      <th>cat15</th>\n",
       "      <th>cat16</th>\n",
       "      <th>cat17</th>\n",
       "      <th>cat18</th>\n",
       "      <th>cat19</th>\n",
       "      <th>cat20</th>\n",
       "      <th>cat21</th>\n",
       "      <th>cat22</th>\n",
       "      <th>cat23</th>\n",
       "      <th>cat24</th>\n",
       "      <th>cat25</th>\n",
       "      <th>cat26</th>\n",
       "      <th>cat27</th>\n",
       "      <th>cat28</th>\n",
       "      <th>cat29</th>\n",
       "      <th>cat30</th>\n",
       "      <th>cat31</th>\n",
       "      <th>cat32</th>\n",
       "      <th>cat33</th>\n",
       "      <th>cat34</th>\n",
       "      <th>cat35</th>\n",
       "      <th>cat36</th>\n",
       "      <th>cat37</th>\n",
       "      <th>cat38</th>\n",
       "      <th>cat39</th>\n",
       "      <th>cat40</th>\n",
       "      <th>cat41</th>\n",
       "      <th>cat42</th>\n",
       "      <th>cat43</th>\n",
       "      <th>cat44</th>\n",
       "      <th>cat45</th>\n",
       "      <th>cat46</th>\n",
       "      <th>cat47</th>\n",
       "      <th>cat48</th>\n",
       "      <th>cat49</th>\n",
       "      <th>cat50</th>\n",
       "      <th>cat51</th>\n",
       "      <th>cat52</th>\n",
       "      <th>cat53</th>\n",
       "      <th>cat54</th>\n",
       "      <th>cat55</th>\n",
       "      <th>cat56</th>\n",
       "      <th>cat57</th>\n",
       "      <th>cat58</th>\n",
       "      <th>cat59</th>\n",
       "      <th>cat60</th>\n",
       "      <th>cat61</th>\n",
       "      <th>cat62</th>\n",
       "      <th>cat63</th>\n",
       "      <th>cat64</th>\n",
       "      <th>cat65</th>\n",
       "      <th>cat66</th>\n",
       "      <th>cat67</th>\n",
       "      <th>cat68</th>\n",
       "      <th>cat69</th>\n",
       "      <th>cat70</th>\n",
       "      <th>cat71</th>\n",
       "      <th>cat72</th>\n",
       "      <th>cat73</th>\n",
       "      <th>cat74</th>\n",
       "      <th>cat75</th>\n",
       "      <th>cat76</th>\n",
       "      <th>cat77</th>\n",
       "      <th>cat78</th>\n",
       "      <th>cat79</th>\n",
       "      <th>cat80</th>\n",
       "      <th>cat81</th>\n",
       "      <th>cat82</th>\n",
       "      <th>cat83</th>\n",
       "      <th>cat84</th>\n",
       "      <th>cat85</th>\n",
       "      <th>cat86</th>\n",
       "      <th>cat87</th>\n",
       "      <th>cat88</th>\n",
       "      <th>cat89</th>\n",
       "      <th>cat90</th>\n",
       "      <th>cat91</th>\n",
       "      <th>cat92</th>\n",
       "      <th>cat93</th>\n",
       "      <th>cat94</th>\n",
       "      <th>cat95</th>\n",
       "      <th>cat96</th>\n",
       "      <th>cat97</th>\n",
       "      <th>cat98</th>\n",
       "      <th>cat99</th>\n",
       "      <th>cat100</th>\n",
       "      <th>cat101</th>\n",
       "      <th>cat102</th>\n",
       "      <th>cat103</th>\n",
       "      <th>cat104</th>\n",
       "      <th>cat105</th>\n",
       "      <th>cat106</th>\n",
       "      <th>cat107</th>\n",
       "      <th>cat108</th>\n",
       "      <th>cat109</th>\n",
       "      <th>cat110</th>\n",
       "      <th>cat111</th>\n",
       "      <th>cat112</th>\n",
       "      <th>cat113</th>\n",
       "      <th>cat114</th>\n",
       "      <th>cat115</th>\n",
       "      <th>cat116</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>269</td>\n",
       "      <td>0.726300</td>\n",
       "      <td>0.245921</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>0.789639</td>\n",
       "      <td>0.310061</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.335060</td>\n",
       "      <td>0.30260</td>\n",
       "      <td>0.67135</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.569745</td>\n",
       "      <td>0.594646</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>0.714843</td>\n",
       "      <td>2213.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>85</td>\n",
       "      <td>0.330514</td>\n",
       "      <td>0.737068</td>\n",
       "      <td>0.592681</td>\n",
       "      <td>0.614134</td>\n",
       "      <td>0.885834</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.436585</td>\n",
       "      <td>0.60087</td>\n",
       "      <td>0.35127</td>\n",
       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
       "      <td>0.366307</td>\n",
       "      <td>0.611431</td>\n",
       "      <td>0.304496</td>\n",
       "      <td>1283.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>153</td>\n",
       "      <td>0.261841</td>\n",
       "      <td>0.358319</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>0.236924</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.289648</td>\n",
       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "      <td>3005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>0.321594</td>\n",
       "      <td>0.555782</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.422268</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
       "      <td>939.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0.273204</td>\n",
       "      <td>0.159990</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.473202</td>\n",
       "      <td>0.704268</td>\n",
       "      <td>0.178193</td>\n",
       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>2763.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8  cat9  cat10  cat11  \\\n",
       "0   1     0     1     0     1     0     0     0     0     1      0      1   \n",
       "1   2     0     1     0     0     0     0     0     0     1      1      0   \n",
       "2   5     0     1     0     0     1     0     0     0     1      1      1   \n",
       "3  10     1     1     0     1     0     0     0     0     1      0      0   \n",
       "4  11     0     1     0     1     0     0     0     0     1      1      0   \n",
       "\n",
       "   cat12  cat13  cat14  cat15  cat16  cat17  cat18  cat19  cat20  cat21  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      1      1      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      1      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat22  cat23  cat24  cat25  cat26  cat27  cat28  cat29  cat30  cat31  \\\n",
       "0      0      1      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      1      0      0      0      0      0      0      0      0   \n",
       "4      0      1      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat32  cat33  cat34  cat35  cat36  cat37  cat38  cat39  cat40  cat41  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      1      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat42  cat43  cat44  cat45  cat46  cat47  cat48  cat49  cat50  cat51  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat52  cat53  cat54  cat55  cat56  cat57  cat58  cat59  cat60  cat61  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat62  cat63  cat64  cat65  cat66  cat67  cat68  cat69  cat70  cat71  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat72  cat73  cat74  cat75  cat76  cat77  cat78  cat79  cat80  cat81  \\\n",
       "0      0      0      0      1      0      3      1      1      3      3   \n",
       "1      0      0      0      0      0      3      1      1      3      3   \n",
       "2      0      0      0      0      0      3      1      1      1      3   \n",
       "3      0      1      0      0      0      3      1      1      3      3   \n",
       "4      1      0      0      0      0      3      1      3      1      3   \n",
       "\n",
       "   cat82  cat83  cat84  cat85  cat86  cat87  cat88  cat89  cat90  cat91  \\\n",
       "0      1      3      2      1      3      1      0      0      0      0   \n",
       "1      0      1      2      1      3      1      0      0      0      0   \n",
       "2      1      3      2      1      1      1      0      0      0      0   \n",
       "3      3      1      2      1      3      1      0      0      0      0   \n",
       "4      1      1      2      1      1      2      0      0      0      1   \n",
       "\n",
       "   cat92  cat93  cat94  cat95  cat96  cat97  cat98  cat99  cat100  cat101  \\\n",
       "0      0      3      1      2      4      0      2     15       1       6   \n",
       "1      0      3      3      2      4      4      3     15      11       5   \n",
       "2      0      3      3      2      4      4      0      1      11      14   \n",
       "3      0      3      3      2      4      4      3     15       8       3   \n",
       "4      5      3      1      3      4      4      0     12       5       9   \n",
       "\n",
       "   cat102  cat103  cat104  cat105  cat106  cat107  cat108  cat109  cat110  \\\n",
       "0       0       0       8       4       6       9       6      45      28   \n",
       "1       0       0       4       4       8      10      10      33      65   \n",
       "2       0       1       4       5       7       5       0       2      85   \n",
       "3       0       0       4       4       8      10      10      33      67   \n",
       "4       0       0       3       4      10       6       1      66      50   \n",
       "\n",
       "   cat111  cat112  cat113  cat114  cat115  cat116     cont1     cont2  \\\n",
       "0       2      19      55       0      14     269  0.726300  0.245921   \n",
       "1       0      22      38       0      14      85  0.330514  0.737068   \n",
       "2       0      28       5       0       8     153  0.261841  0.358319   \n",
       "3       2      39       4       0      14      79  0.321594  0.555782   \n",
       "4       2      50      38       0      10      55  0.273204  0.159990   \n",
       "\n",
       "      cont3     cont4     cont5     cont6     cont7    cont8    cont9  \\\n",
       "0  0.187583  0.789639  0.310061  0.718367  0.335060  0.30260  0.67135   \n",
       "1  0.592681  0.614134  0.885834  0.438917  0.436585  0.60087  0.35127   \n",
       "2  0.484196  0.236924  0.397069  0.289648  0.315545  0.27320  0.26076   \n",
       "3  0.527991  0.373816  0.422268  0.440945  0.391128  0.31796  0.32128   \n",
       "4  0.527991  0.473202  0.704268  0.178193  0.247408  0.24564  0.22089   \n",
       "\n",
       "    cont10    cont11    cont12    cont13    cont14     loss  \n",
       "0  0.83510  0.569745  0.594646  0.822493  0.714843  2213.18  \n",
       "1  0.43919  0.338312  0.366307  0.611431  0.304496  1283.60  \n",
       "2  0.32446  0.381398  0.373424  0.195709  0.774425  3005.09  \n",
       "3  0.44467  0.327915  0.321570  0.605077  0.602642   939.85  \n",
       "4  0.21230  0.204687  0.202213  0.246011  0.432606  2763.85  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 57.5 ms\n"
     ]
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 353 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "X, y = train[train.columns.delete(-1)],train['loss']\n",
    "X.drop('id',axis=1,inplace=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCORE:0.4809\n",
      "RMSE:2076.5137\n",
      "MAE:36.4674\n",
      "time: 2.3 s\n"
     ]
    }
   ],
   "source": [
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, y_train)\n",
    "coef = linear_model.coef_#回归系数\n",
    "line_pre = linear_model.predict(X_test)\n",
    "print('SCORE:{:.4f}'.format(linear_model.score(X_test, y_test)))\n",
    "print('RMSE:{:.4f}'.format(np.sqrt(mean_squared_error(y_test, line_pre))))\n",
    "print('MAE:{:.4f}'.format(np.sqrt(mean_absolute_error(y_test,line_pre))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat1</td>\n",
       "      <td>-409.208216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat2</td>\n",
       "      <td>320.889215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat3</td>\n",
       "      <td>245.817392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat4</td>\n",
       "      <td>64.107341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat5</td>\n",
       "      <td>94.290663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat6</td>\n",
       "      <td>-330.915562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat7</td>\n",
       "      <td>679.176075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat8</td>\n",
       "      <td>131.800018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cat9</td>\n",
       "      <td>34.642249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cat10</td>\n",
       "      <td>160.720535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cat11</td>\n",
       "      <td>100.664003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cat12</td>\n",
       "      <td>687.294936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cat13</td>\n",
       "      <td>73.512615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cat14</td>\n",
       "      <td>1.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cat15</td>\n",
       "      <td>-480.432649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cat16</td>\n",
       "      <td>-5160.800428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cat17</td>\n",
       "      <td>-4718.430619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cat18</td>\n",
       "      <td>-5052.968346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>cat19</td>\n",
       "      <td>-4542.628960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cat20</td>\n",
       "      <td>-4893.629473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>cat21</td>\n",
       "      <td>-5440.116179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cat22</td>\n",
       "      <td>-6009.134974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cat23</td>\n",
       "      <td>409.412067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cat24</td>\n",
       "      <td>222.710779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>cat25</td>\n",
       "      <td>573.845088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>cat26</td>\n",
       "      <td>860.357681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cat27</td>\n",
       "      <td>605.532940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>cat28</td>\n",
       "      <td>247.399872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cat29</td>\n",
       "      <td>561.559144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cat30</td>\n",
       "      <td>245.144759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cat31</td>\n",
       "      <td>191.055605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cat32</td>\n",
       "      <td>763.411871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>cat33</td>\n",
       "      <td>387.762515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>cat34</td>\n",
       "      <td>700.043305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>cat35</td>\n",
       "      <td>1060.100383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>cat36</td>\n",
       "      <td>-7836.146066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>cat37</td>\n",
       "      <td>-7669.218981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>cat38</td>\n",
       "      <td>-7527.321862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>cat39</td>\n",
       "      <td>-8088.120740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>cat40</td>\n",
       "      <td>-7962.816938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>cat41</td>\n",
       "      <td>-8126.423538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>cat42</td>\n",
       "      <td>-7486.744190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>cat43</td>\n",
       "      <td>-7975.018926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>cat44</td>\n",
       "      <td>-7372.777133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>cat45</td>\n",
       "      <td>-7951.680078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>cat46</td>\n",
       "      <td>-7663.814573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>cat47</td>\n",
       "      <td>-7632.618394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>cat48</td>\n",
       "      <td>-7535.901129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>cat49</td>\n",
       "      <td>476.610839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>cat50</td>\n",
       "      <td>388.448690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>cat51</td>\n",
       "      <td>734.140179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>cat52</td>\n",
       "      <td>453.149429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>cat53</td>\n",
       "      <td>875.710111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>cat54</td>\n",
       "      <td>104.760337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>cat55</td>\n",
       "      <td>128.304106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>cat56</td>\n",
       "      <td>597.103434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>cat57</td>\n",
       "      <td>2681.487018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>cat58</td>\n",
       "      <td>-696.234863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>cat59</td>\n",
       "      <td>-594.958030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>cat60</td>\n",
       "      <td>-1342.991991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>cat61</td>\n",
       "      <td>-1530.904214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>cat62</td>\n",
       "      <td>-638.330140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>cat63</td>\n",
       "      <td>-1703.182248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>cat64</td>\n",
       "      <td>17.960429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>cat65</td>\n",
       "      <td>246.335579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>cat66</td>\n",
       "      <td>-181.988964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>cat67</td>\n",
       "      <td>494.953385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>cat68</td>\n",
       "      <td>652.559106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>cat69</td>\n",
       "      <td>-241.128063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>cat70</td>\n",
       "      <td>507.673641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>cat71</td>\n",
       "      <td>239.085045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cat72</td>\n",
       "      <td>361.497009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>cat73</td>\n",
       "      <td>-294.601544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>cat74</td>\n",
       "      <td>64.826538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>cat75</td>\n",
       "      <td>-130.966378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>cat76</td>\n",
       "      <td>159.923691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>cat77</td>\n",
       "      <td>377.635029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>cat78</td>\n",
       "      <td>254.577212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>cat79</td>\n",
       "      <td>640.016132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>cat80</td>\n",
       "      <td>-565.507136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>cat81</td>\n",
       "      <td>-374.208191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>cat82</td>\n",
       "      <td>-174.653579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>cat83</td>\n",
       "      <td>-35.567024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>cat84</td>\n",
       "      <td>-18.035068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>cat85</td>\n",
       "      <td>412.985981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>cat86</td>\n",
       "      <td>112.891639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>cat87</td>\n",
       "      <td>407.437049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>cat88</td>\n",
       "      <td>161.919138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>cat89</td>\n",
       "      <td>795.669202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>cat90</td>\n",
       "      <td>4809.488900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>cat91</td>\n",
       "      <td>8.334047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>cat92</td>\n",
       "      <td>14.196209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>cat93</td>\n",
       "      <td>18.702497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>cat94</td>\n",
       "      <td>-27.677468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>cat95</td>\n",
       "      <td>36.598459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>cat96</td>\n",
       "      <td>30.287545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>cat97</td>\n",
       "      <td>30.710782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>cat98</td>\n",
       "      <td>-57.471183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>cat99</td>\n",
       "      <td>-17.224695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>cat100</td>\n",
       "      <td>13.369145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>cat101</td>\n",
       "      <td>86.961389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>cat102</td>\n",
       "      <td>2.850482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>cat103</td>\n",
       "      <td>8108.255358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>cat104</td>\n",
       "      <td>115.557723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>cat105</td>\n",
       "      <td>171.937421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>cat106</td>\n",
       "      <td>-59.525180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>cat107</td>\n",
       "      <td>-25.977471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>cat108</td>\n",
       "      <td>-6.590334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>cat109</td>\n",
       "      <td>0.980196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>cat110</td>\n",
       "      <td>0.292115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>cat111</td>\n",
       "      <td>-80.125528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>cat112</td>\n",
       "      <td>-4.407042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>cat113</td>\n",
       "      <td>-0.282288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>cat114</td>\n",
       "      <td>29.198737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>cat115</td>\n",
       "      <td>-2.307970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>cat116</td>\n",
       "      <td>-0.082081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>cont1</td>\n",
       "      <td>-1843.949055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>cont2</td>\n",
       "      <td>1438.795702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>cont3</td>\n",
       "      <td>688.271741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>cont4</td>\n",
       "      <td>-417.630535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>cont5</td>\n",
       "      <td>227.314324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>cont6</td>\n",
       "      <td>-59.007938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>cont7</td>\n",
       "      <td>2414.620417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>cont8</td>\n",
       "      <td>334.419896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>cont9</td>\n",
       "      <td>765.499231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>cont10</td>\n",
       "      <td>418.094693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>cont11</td>\n",
       "      <td>-450.405396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>cont12</td>\n",
       "      <td>-1157.738644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>cont13</td>\n",
       "      <td>129.090853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>cont14</td>\n",
       "      <td>674.733097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Title         Coef\n",
       "0      cat1  -409.208216\n",
       "1      cat2   320.889215\n",
       "2      cat3   245.817392\n",
       "3      cat4    64.107341\n",
       "4      cat5    94.290663\n",
       "5      cat6  -330.915562\n",
       "6      cat7   679.176075\n",
       "7      cat8   131.800018\n",
       "8      cat9    34.642249\n",
       "9     cat10   160.720535\n",
       "10    cat11   100.664003\n",
       "11    cat12   687.294936\n",
       "12    cat13    73.512615\n",
       "13    cat14     1.899994\n",
       "14    cat15  -480.432649\n",
       "15    cat16 -5160.800428\n",
       "16    cat17 -4718.430619\n",
       "17    cat18 -5052.968346\n",
       "18    cat19 -4542.628960\n",
       "19    cat20 -4893.629473\n",
       "20    cat21 -5440.116179\n",
       "21    cat22 -6009.134974\n",
       "22    cat23   409.412067\n",
       "23    cat24   222.710779\n",
       "24    cat25   573.845088\n",
       "25    cat26   860.357681\n",
       "26    cat27   605.532940\n",
       "27    cat28   247.399872\n",
       "28    cat29   561.559144\n",
       "29    cat30   245.144759\n",
       "30    cat31   191.055605\n",
       "31    cat32   763.411871\n",
       "32    cat33   387.762515\n",
       "33    cat34   700.043305\n",
       "34    cat35  1060.100383\n",
       "35    cat36 -7836.146066\n",
       "36    cat37 -7669.218981\n",
       "37    cat38 -7527.321862\n",
       "38    cat39 -8088.120740\n",
       "39    cat40 -7962.816938\n",
       "40    cat41 -8126.423538\n",
       "41    cat42 -7486.744190\n",
       "42    cat43 -7975.018926\n",
       "43    cat44 -7372.777133\n",
       "44    cat45 -7951.680078\n",
       "45    cat46 -7663.814573\n",
       "46    cat47 -7632.618394\n",
       "47    cat48 -7535.901129\n",
       "48    cat49   476.610839\n",
       "49    cat50   388.448690\n",
       "50    cat51   734.140179\n",
       "51    cat52   453.149429\n",
       "52    cat53   875.710111\n",
       "53    cat54   104.760337\n",
       "54    cat55   128.304106\n",
       "55    cat56   597.103434\n",
       "56    cat57  2681.487018\n",
       "57    cat58  -696.234863\n",
       "58    cat59  -594.958030\n",
       "59    cat60 -1342.991991\n",
       "60    cat61 -1530.904214\n",
       "61    cat62  -638.330140\n",
       "62    cat63 -1703.182248\n",
       "63    cat64    17.960429\n",
       "64    cat65   246.335579\n",
       "65    cat66  -181.988964\n",
       "66    cat67   494.953385\n",
       "67    cat68   652.559106\n",
       "68    cat69  -241.128063\n",
       "69    cat70   507.673641\n",
       "70    cat71   239.085045\n",
       "71    cat72   361.497009\n",
       "72    cat73  -294.601544\n",
       "73    cat74    64.826538\n",
       "74    cat75  -130.966378\n",
       "75    cat76   159.923691\n",
       "76    cat77   377.635029\n",
       "77    cat78   254.577212\n",
       "78    cat79   640.016132\n",
       "79    cat80  -565.507136\n",
       "80    cat81  -374.208191\n",
       "81    cat82  -174.653579\n",
       "82    cat83   -35.567024\n",
       "83    cat84   -18.035068\n",
       "84    cat85   412.985981\n",
       "85    cat86   112.891639\n",
       "86    cat87   407.437049\n",
       "87    cat88   161.919138\n",
       "88    cat89   795.669202\n",
       "89    cat90  4809.488900\n",
       "90    cat91     8.334047\n",
       "91    cat92    14.196209\n",
       "92    cat93    18.702497\n",
       "93    cat94   -27.677468\n",
       "94    cat95    36.598459\n",
       "95    cat96    30.287545\n",
       "96    cat97    30.710782\n",
       "97    cat98   -57.471183\n",
       "98    cat99   -17.224695\n",
       "99   cat100    13.369145\n",
       "100  cat101    86.961389\n",
       "101  cat102     2.850482\n",
       "102  cat103  8108.255358\n",
       "103  cat104   115.557723\n",
       "104  cat105   171.937421\n",
       "105  cat106   -59.525180\n",
       "106  cat107   -25.977471\n",
       "107  cat108    -6.590334\n",
       "108  cat109     0.980196\n",
       "109  cat110     0.292115\n",
       "110  cat111   -80.125528\n",
       "111  cat112    -4.407042\n",
       "112  cat113    -0.282288\n",
       "113  cat114    29.198737\n",
       "114  cat115    -2.307970\n",
       "115  cat116    -0.082081\n",
       "116   cont1 -1843.949055\n",
       "117   cont2  1438.795702\n",
       "118   cont3   688.271741\n",
       "119   cont4  -417.630535\n",
       "120   cont5   227.314324\n",
       "121   cont6   -59.007938\n",
       "122   cont7  2414.620417\n",
       "123   cont8   334.419896\n",
       "124   cont9   765.499231\n",
       "125  cont10   418.094693\n",
       "126  cont11  -450.405396\n",
       "127  cont12 -1157.738644\n",
       "128  cont13   129.090853\n",
       "129  cont14   674.733097"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 44.1 ms\n"
     ]
    }
   ],
   "source": [
    "train.drop('id',axis=1,inplace=True)\n",
    "df_coef = pd.DataFrame()\n",
    "df_coef['Title'] = train.columns.delete(-1)\n",
    "df_coef['Coef'] = coef\n",
    "df_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a9cae0d10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.01 s\n"
     ]
    }
   ],
   "source": [
    "hos_pre = pd.DataFrame()\n",
    "hos_pre['Predict'] = line_pre\n",
    "hos_pre['Truth'] = y_test.reset_index(drop=True)\n",
    "hos_pre.plot(figsize=(18,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.54 ms\n"
     ]
    }
   ],
   "source": [
    "#RMSE algorithims prefer symmetric data\n",
    "train['log_loss'] = np.log(train['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 140 ms\n"
     ]
    }
   ],
   "source": [
    "X = train[train.columns.delete(-1)]\n",
    "X = X[X.columns.delete(-1)]\n",
    "y = train['log_loss']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.34 s\n"
     ]
    }
   ],
   "source": [
    "linear_model_loss = LinearRegression()\n",
    "linear_model_loss.fit(X_train, y_train)\n",
    "coef = linear_model_loss.coef_#回归系数\n",
    "predicted = linear_model_loss.predict(X_test)\n",
    "y_test = np.exp(y_test)\n",
    "predicted = np.exp(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:2268.5460\n",
      "MAE:1284.6792\n",
      "time: 2.33 ms\n"
     ]
    }
   ],
   "source": [
    "print('RMSE:{:.4f}'.format(np.sqrt(mean_squared_error(y_test, predicted))))\n",
    "print(\"MAE:{:.4f}\".format(mean_absolute_error(y_test,predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125546 entries, 0 to 125545\n",
      "Columns: 131 entries, id to cont14\n",
      "dtypes: float64(14), int64(1), object(116)\n",
      "memory usage: 125.5+ MB\n",
      "time: 14.1 ms\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>cat10</th>\n",
       "      <th>cat11</th>\n",
       "      <th>cat12</th>\n",
       "      <th>cat13</th>\n",
       "      <th>cat14</th>\n",
       "      <th>cat15</th>\n",
       "      <th>cat16</th>\n",
       "      <th>cat17</th>\n",
       "      <th>cat18</th>\n",
       "      <th>cat19</th>\n",
       "      <th>cat20</th>\n",
       "      <th>cat21</th>\n",
       "      <th>cat22</th>\n",
       "      <th>cat23</th>\n",
       "      <th>cat24</th>\n",
       "      <th>cat25</th>\n",
       "      <th>cat26</th>\n",
       "      <th>cat27</th>\n",
       "      <th>cat28</th>\n",
       "      <th>cat29</th>\n",
       "      <th>cat30</th>\n",
       "      <th>cat31</th>\n",
       "      <th>cat32</th>\n",
       "      <th>cat33</th>\n",
       "      <th>cat34</th>\n",
       "      <th>cat35</th>\n",
       "      <th>cat36</th>\n",
       "      <th>cat37</th>\n",
       "      <th>cat38</th>\n",
       "      <th>cat39</th>\n",
       "      <th>cat40</th>\n",
       "      <th>cat41</th>\n",
       "      <th>cat42</th>\n",
       "      <th>cat43</th>\n",
       "      <th>cat44</th>\n",
       "      <th>cat45</th>\n",
       "      <th>cat46</th>\n",
       "      <th>cat47</th>\n",
       "      <th>cat48</th>\n",
       "      <th>cat49</th>\n",
       "      <th>cat50</th>\n",
       "      <th>cat51</th>\n",
       "      <th>cat52</th>\n",
       "      <th>cat53</th>\n",
       "      <th>cat54</th>\n",
       "      <th>cat55</th>\n",
       "      <th>cat56</th>\n",
       "      <th>cat57</th>\n",
       "      <th>cat58</th>\n",
       "      <th>cat59</th>\n",
       "      <th>cat60</th>\n",
       "      <th>cat61</th>\n",
       "      <th>cat62</th>\n",
       "      <th>cat63</th>\n",
       "      <th>cat64</th>\n",
       "      <th>cat65</th>\n",
       "      <th>cat66</th>\n",
       "      <th>cat67</th>\n",
       "      <th>cat68</th>\n",
       "      <th>cat69</th>\n",
       "      <th>cat70</th>\n",
       "      <th>cat71</th>\n",
       "      <th>cat72</th>\n",
       "      <th>cat73</th>\n",
       "      <th>cat74</th>\n",
       "      <th>cat75</th>\n",
       "      <th>cat76</th>\n",
       "      <th>cat77</th>\n",
       "      <th>cat78</th>\n",
       "      <th>cat79</th>\n",
       "      <th>cat80</th>\n",
       "      <th>cat81</th>\n",
       "      <th>cat82</th>\n",
       "      <th>cat83</th>\n",
       "      <th>cat84</th>\n",
       "      <th>cat85</th>\n",
       "      <th>cat86</th>\n",
       "      <th>cat87</th>\n",
       "      <th>cat88</th>\n",
       "      <th>cat89</th>\n",
       "      <th>cat90</th>\n",
       "      <th>cat91</th>\n",
       "      <th>cat92</th>\n",
       "      <th>cat93</th>\n",
       "      <th>cat94</th>\n",
       "      <th>cat95</th>\n",
       "      <th>cat96</th>\n",
       "      <th>cat97</th>\n",
       "      <th>cat98</th>\n",
       "      <th>cat99</th>\n",
       "      <th>cat100</th>\n",
       "      <th>cat101</th>\n",
       "      <th>cat102</th>\n",
       "      <th>cat103</th>\n",
       "      <th>cat104</th>\n",
       "      <th>cat105</th>\n",
       "      <th>cat106</th>\n",
       "      <th>cat107</th>\n",
       "      <th>cat108</th>\n",
       "      <th>cat109</th>\n",
       "      <th>cat110</th>\n",
       "      <th>cat111</th>\n",
       "      <th>cat112</th>\n",
       "      <th>cat113</th>\n",
       "      <th>cat114</th>\n",
       "      <th>cat115</th>\n",
       "      <th>cat116</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>T</td>\n",
       "      <td>H</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>E</td>\n",
       "      <td>I</td>\n",
       "      <td>L</td>\n",
       "      <td>K</td>\n",
       "      <td>BI</td>\n",
       "      <td>BC</td>\n",
       "      <td>A</td>\n",
       "      <td>J</td>\n",
       "      <td>AX</td>\n",
       "      <td>A</td>\n",
       "      <td>Q</td>\n",
       "      <td>HG</td>\n",
       "      <td>0.321594</td>\n",
       "      <td>0.299102</td>\n",
       "      <td>0.246911</td>\n",
       "      <td>0.402922</td>\n",
       "      <td>0.281143</td>\n",
       "      <td>0.466591</td>\n",
       "      <td>0.317681</td>\n",
       "      <td>0.61229</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.38016</td>\n",
       "      <td>0.377724</td>\n",
       "      <td>0.369858</td>\n",
       "      <td>0.704052</td>\n",
       "      <td>0.392562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>P</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
       "      <td>BI</td>\n",
       "      <td>CO</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>X</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>HK</td>\n",
       "      <td>0.634734</td>\n",
       "      <td>0.620805</td>\n",
       "      <td>0.654310</td>\n",
       "      <td>0.946616</td>\n",
       "      <td>0.836443</td>\n",
       "      <td>0.482425</td>\n",
       "      <td>0.443760</td>\n",
       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
       "      <td>0.689039</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.453468</td>\n",
       "      <td>0.208045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>G</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>J</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>BI</td>\n",
       "      <td>CS</td>\n",
       "      <td>C</td>\n",
       "      <td>U</td>\n",
       "      <td>AE</td>\n",
       "      <td>A</td>\n",
       "      <td>K</td>\n",
       "      <td>CK</td>\n",
       "      <td>0.290813</td>\n",
       "      <td>0.737068</td>\n",
       "      <td>0.711159</td>\n",
       "      <td>0.412789</td>\n",
       "      <td>0.718531</td>\n",
       "      <td>0.212308</td>\n",
       "      <td>0.325779</td>\n",
       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>H</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>I</td>\n",
       "      <td>K</td>\n",
       "      <td>K</td>\n",
       "      <td>BI</td>\n",
       "      <td>CR</td>\n",
       "      <td>A</td>\n",
       "      <td>AY</td>\n",
       "      <td>AJ</td>\n",
       "      <td>A</td>\n",
       "      <td>P</td>\n",
       "      <td>DJ</td>\n",
       "      <td>0.268622</td>\n",
       "      <td>0.681761</td>\n",
       "      <td>0.592681</td>\n",
       "      <td>0.354893</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.369930</td>\n",
       "      <td>0.342355</td>\n",
       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>P</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>E</td>\n",
       "      <td>B</td>\n",
       "      <td>AB</td>\n",
       "      <td>EG</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>I</td>\n",
       "      <td>C</td>\n",
       "      <td>J</td>\n",
       "      <td>HA</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.299102</td>\n",
       "      <td>0.263570</td>\n",
       "      <td>0.696873</td>\n",
       "      <td>0.302678</td>\n",
       "      <td>0.398862</td>\n",
       "      <td>0.391833</td>\n",
       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 cat10 cat11 cat12 cat13 cat14  \\\n",
       "0    A    B    A    A    A    A    A    A    B     A     B     A     A     A   \n",
       "1    A    B    A    B    A    A    A    A    B     A     A     A     A     A   \n",
       "2    A    B    A    B    B    A    B    A    B     B     A     B     B     B   \n",
       "3    A    A    A    A    B    A    A    A    A     A     A     A     A     A   \n",
       "4    B    A    A    A    A    B    A    A    A     A     A     A     A     A   \n",
       "\n",
       "  cat15 cat16 cat17 cat18 cat19 cat20 cat21 cat22 cat23 cat24 cat25 cat26  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     B     B     A     A   \n",
       "2     A     A     A     A     A     A     A     A     B     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat27 cat28 cat29 cat30 cat31 cat32 cat33 cat34 cat35 cat36 cat37 cat38  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     B     A     B   \n",
       "3     A     A     A     A     A     A     A     A     A     B     A     B   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat39 cat40 cat41 cat42 cat43 cat44 cat45 cat46 cat47 cat48 cat49 cat50  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     B     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     B     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     B   \n",
       "\n",
       "  cat51 cat52 cat53 cat54 cat55 cat56 cat57 cat58 cat59 cat60 cat61 cat62  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "2     A     A     A     A     A     A     B     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat63 cat64 cat65 cat66 cat67 cat68 cat69 cat70 cat71 cat72 cat73 cat74  \\\n",
       "0     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "1     A     A     A     A     A     A     A     A     A     B     A     A   \n",
       "2     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "3     A     A     A     A     A     A     A     A     A     B     A     A   \n",
       "4     A     A     A     A     A     A     A     A     A     A     A     A   \n",
       "\n",
       "  cat75 cat76 cat77 cat78 cat79 cat80 cat81 cat82 cat83 cat84 cat85 cat86  \\\n",
       "0     A     A     D     B     B     D     D     B     B     C     B     D   \n",
       "1     B     A     D     B     B     D     D     B     B     C     B     B   \n",
       "2     A     B     D     B     B     B     B     B     D     C     B     B   \n",
       "3     A     A     D     B     D     B     D     B     B     A     B     D   \n",
       "4     A     A     D     B     B     D     D     B     B     C     B     B   \n",
       "\n",
       "  cat87 cat88 cat89 cat90 cat91 cat92 cat93 cat94 cat95 cat96 cat97 cat98  \\\n",
       "0     B     A     A     A     A     A     D     C     C     E     C     D   \n",
       "1     B     A     A     A     A     A     D     D     D     E     A     A   \n",
       "2     B     A     B     A     A     A     D     D     C     E     E     A   \n",
       "3     D     A     A     A     G     H     D     D     C     E     E     D   \n",
       "4     B     A     A     A     A     A     D     B     D     E     A     A   \n",
       "\n",
       "  cat99 cat100 cat101 cat102 cat103 cat104 cat105 cat106 cat107 cat108 cat109  \\\n",
       "0     T      H      G      A      A      G      E      I      L      K     BI   \n",
       "1     P      B      D      A      A      G      G      G      F      B     BI   \n",
       "2     D      G      Q      A      D      D      E      J      G      A     BI   \n",
       "3     T      G      A      A      D      E      E      I      K      K     BI   \n",
       "4     P      A      A      A      A      F      E      G      E      B     AB   \n",
       "\n",
       "  cat110 cat111 cat112 cat113 cat114 cat115 cat116     cont1     cont2  \\\n",
       "0     BC      A      J     AX      A      Q     HG  0.321594  0.299102   \n",
       "1     CO      E      G      X      A      L     HK  0.634734  0.620805   \n",
       "2     CS      C      U     AE      A      K     CK  0.290813  0.737068   \n",
       "3     CR      A     AY     AJ      A      P     DJ  0.268622  0.681761   \n",
       "4     EG      A      E      I      C      J     HA  0.553846  0.299102   \n",
       "\n",
       "      cont3     cont4     cont5     cont6     cont7    cont8    cont9  \\\n",
       "0  0.246911  0.402922  0.281143  0.466591  0.317681  0.61229  0.34365   \n",
       "1  0.654310  0.946616  0.836443  0.482425  0.443760  0.71330  0.51890   \n",
       "2  0.711159  0.412789  0.718531  0.212308  0.325779  0.29758  0.34365   \n",
       "3  0.592681  0.354893  0.397069  0.369930  0.342355  0.40028  0.33237   \n",
       "4  0.263570  0.696873  0.302678  0.398862  0.391833  0.23688  0.43731   \n",
       "\n",
       "    cont10    cont11    cont12    cont13    cont14  \n",
       "0  0.38016  0.377724  0.369858  0.704052  0.392562  \n",
       "1  0.60401  0.689039  0.675759  0.453468  0.208045  \n",
       "2  0.30529  0.245410  0.241676  0.258586  0.297232  \n",
       "3  0.31480  0.348867  0.341872  0.592264  0.555955  \n",
       "4  0.50556  0.359572  0.352251  0.301535  0.825823  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.1 s\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('../data_files/4.Allstate_data/test.csv')\n",
    "test_id = test['id']\n",
    "test.drop('id',axis=1,inplace=True)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.94 s\n"
     ]
    }
   ],
   "source": [
    "for c in range(len(cat_features)):\n",
    "    test[cat_features[c]] = train[cat_features[c]].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>cat10</th>\n",
       "      <th>cat11</th>\n",
       "      <th>cat12</th>\n",
       "      <th>cat13</th>\n",
       "      <th>cat14</th>\n",
       "      <th>cat15</th>\n",
       "      <th>cat16</th>\n",
       "      <th>cat17</th>\n",
       "      <th>cat18</th>\n",
       "      <th>cat19</th>\n",
       "      <th>cat20</th>\n",
       "      <th>cat21</th>\n",
       "      <th>cat22</th>\n",
       "      <th>cat23</th>\n",
       "      <th>cat24</th>\n",
       "      <th>cat25</th>\n",
       "      <th>cat26</th>\n",
       "      <th>cat27</th>\n",
       "      <th>cat28</th>\n",
       "      <th>cat29</th>\n",
       "      <th>cat30</th>\n",
       "      <th>cat31</th>\n",
       "      <th>cat32</th>\n",
       "      <th>cat33</th>\n",
       "      <th>cat34</th>\n",
       "      <th>cat35</th>\n",
       "      <th>cat36</th>\n",
       "      <th>cat37</th>\n",
       "      <th>cat38</th>\n",
       "      <th>cat39</th>\n",
       "      <th>cat40</th>\n",
       "      <th>cat41</th>\n",
       "      <th>cat42</th>\n",
       "      <th>cat43</th>\n",
       "      <th>cat44</th>\n",
       "      <th>cat45</th>\n",
       "      <th>cat46</th>\n",
       "      <th>cat47</th>\n",
       "      <th>cat48</th>\n",
       "      <th>cat49</th>\n",
       "      <th>cat50</th>\n",
       "      <th>cat51</th>\n",
       "      <th>cat52</th>\n",
       "      <th>cat53</th>\n",
       "      <th>cat54</th>\n",
       "      <th>cat55</th>\n",
       "      <th>cat56</th>\n",
       "      <th>cat57</th>\n",
       "      <th>cat58</th>\n",
       "      <th>cat59</th>\n",
       "      <th>cat60</th>\n",
       "      <th>cat61</th>\n",
       "      <th>cat62</th>\n",
       "      <th>cat63</th>\n",
       "      <th>cat64</th>\n",
       "      <th>cat65</th>\n",
       "      <th>cat66</th>\n",
       "      <th>cat67</th>\n",
       "      <th>cat68</th>\n",
       "      <th>cat69</th>\n",
       "      <th>cat70</th>\n",
       "      <th>cat71</th>\n",
       "      <th>cat72</th>\n",
       "      <th>cat73</th>\n",
       "      <th>cat74</th>\n",
       "      <th>cat75</th>\n",
       "      <th>cat76</th>\n",
       "      <th>cat77</th>\n",
       "      <th>cat78</th>\n",
       "      <th>cat79</th>\n",
       "      <th>cat80</th>\n",
       "      <th>cat81</th>\n",
       "      <th>cat82</th>\n",
       "      <th>cat83</th>\n",
       "      <th>cat84</th>\n",
       "      <th>cat85</th>\n",
       "      <th>cat86</th>\n",
       "      <th>cat87</th>\n",
       "      <th>cat88</th>\n",
       "      <th>cat89</th>\n",
       "      <th>cat90</th>\n",
       "      <th>cat91</th>\n",
       "      <th>cat92</th>\n",
       "      <th>cat93</th>\n",
       "      <th>cat94</th>\n",
       "      <th>cat95</th>\n",
       "      <th>cat96</th>\n",
       "      <th>cat97</th>\n",
       "      <th>cat98</th>\n",
       "      <th>cat99</th>\n",
       "      <th>cat100</th>\n",
       "      <th>cat101</th>\n",
       "      <th>cat102</th>\n",
       "      <th>cat103</th>\n",
       "      <th>cat104</th>\n",
       "      <th>cat105</th>\n",
       "      <th>cat106</th>\n",
       "      <th>cat107</th>\n",
       "      <th>cat108</th>\n",
       "      <th>cat109</th>\n",
       "      <th>cat110</th>\n",
       "      <th>cat111</th>\n",
       "      <th>cat112</th>\n",
       "      <th>cat113</th>\n",
       "      <th>cat114</th>\n",
       "      <th>cat115</th>\n",
       "      <th>cat116</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>269</td>\n",
       "      <td>0.321594</td>\n",
       "      <td>0.299102</td>\n",
       "      <td>0.246911</td>\n",
       "      <td>0.402922</td>\n",
       "      <td>0.281143</td>\n",
       "      <td>0.466591</td>\n",
       "      <td>0.317681</td>\n",
       "      <td>0.61229</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.38016</td>\n",
       "      <td>0.377724</td>\n",
       "      <td>0.369858</td>\n",
       "      <td>0.704052</td>\n",
       "      <td>0.392562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>85</td>\n",
       "      <td>0.634734</td>\n",
       "      <td>0.620805</td>\n",
       "      <td>0.654310</td>\n",
       "      <td>0.946616</td>\n",
       "      <td>0.836443</td>\n",
       "      <td>0.482425</td>\n",
       "      <td>0.443760</td>\n",
       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
       "      <td>0.689039</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.453468</td>\n",
       "      <td>0.208045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>153</td>\n",
       "      <td>0.290813</td>\n",
       "      <td>0.737068</td>\n",
       "      <td>0.711159</td>\n",
       "      <td>0.412789</td>\n",
       "      <td>0.718531</td>\n",
       "      <td>0.212308</td>\n",
       "      <td>0.325779</td>\n",
       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>0.268622</td>\n",
       "      <td>0.681761</td>\n",
       "      <td>0.592681</td>\n",
       "      <td>0.354893</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.369930</td>\n",
       "      <td>0.342355</td>\n",
       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.299102</td>\n",
       "      <td>0.263570</td>\n",
       "      <td>0.696873</td>\n",
       "      <td>0.302678</td>\n",
       "      <td>0.398862</td>\n",
       "      <td>0.391833</td>\n",
       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8  cat9  cat10  cat11  cat12  \\\n",
       "0     0     1     0     1     0     0     0     0     1      0      1      0   \n",
       "1     0     1     0     0     0     0     0     0     1      1      0      0   \n",
       "2     0     1     0     0     1     0     0     0     1      1      1      1   \n",
       "3     1     1     0     1     0     0     0     0     1      0      0      0   \n",
       "4     0     1     0     1     0     0     0     0     1      1      0      1   \n",
       "\n",
       "   cat13  cat14  cat15  cat16  cat17  cat18  cat19  cat20  cat21  cat22  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      1      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat23  cat24  cat25  cat26  cat27  cat28  cat29  cat30  cat31  cat32  \\\n",
       "0      1      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      1      0      0      0      0      0      0      0      0      0   \n",
       "4      1      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat33  cat34  cat35  cat36  cat37  cat38  cat39  cat40  cat41  cat42  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      1      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat43  cat44  cat45  cat46  cat47  cat48  cat49  cat50  cat51  cat52  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat53  cat54  cat55  cat56  cat57  cat58  cat59  cat60  cat61  cat62  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat63  cat64  cat65  cat66  cat67  cat68  cat69  cat70  cat71  cat72  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      1   \n",
       "\n",
       "   cat73  cat74  cat75  cat76  cat77  cat78  cat79  cat80  cat81  cat82  \\\n",
       "0      0      0      1      0      3      1      1      3      3      1   \n",
       "1      0      0      0      0      3      1      1      3      3      0   \n",
       "2      0      0      0      0      3      1      1      1      3      1   \n",
       "3      1      0      0      0      3      1      1      3      3      3   \n",
       "4      0      0      0      0      3      1      3      1      3      1   \n",
       "\n",
       "   cat83  cat84  cat85  cat86  cat87  cat88  cat89  cat90  cat91  cat92  \\\n",
       "0      3      2      1      3      1      0      0      0      0      0   \n",
       "1      1      2      1      3      1      0      0      0      0      0   \n",
       "2      3      2      1      1      1      0      0      0      0      0   \n",
       "3      1      2      1      3      1      0      0      0      0      0   \n",
       "4      1      2      1      1      2      0      0      0      1      5   \n",
       "\n",
       "   cat93  cat94  cat95  cat96  cat97  cat98  cat99  cat100  cat101  cat102  \\\n",
       "0      3      1      2      4      0      2     15       1       6       0   \n",
       "1      3      3      2      4      4      3     15      11       5       0   \n",
       "2      3      3      2      4      4      0      1      11      14       0   \n",
       "3      3      3      2      4      4      3     15       8       3       0   \n",
       "4      3      1      3      4      4      0     12       5       9       0   \n",
       "\n",
       "   cat103  cat104  cat105  cat106  cat107  cat108  cat109  cat110  cat111  \\\n",
       "0       0       8       4       6       9       6      45      28       2   \n",
       "1       0       4       4       8      10      10      33      65       0   \n",
       "2       1       4       5       7       5       0       2      85       0   \n",
       "3       0       4       4       8      10      10      33      67       2   \n",
       "4       0       3       4      10       6       1      66      50       2   \n",
       "\n",
       "   cat112  cat113  cat114  cat115  cat116     cont1     cont2     cont3  \\\n",
       "0      19      55       0      14     269  0.321594  0.299102  0.246911   \n",
       "1      22      38       0      14      85  0.634734  0.620805  0.654310   \n",
       "2      28       5       0       8     153  0.290813  0.737068  0.711159   \n",
       "3      39       4       0      14      79  0.268622  0.681761  0.592681   \n",
       "4      50      38       0      10      55  0.553846  0.299102  0.263570   \n",
       "\n",
       "      cont4     cont5     cont6     cont7    cont8    cont9   cont10  \\\n",
       "0  0.402922  0.281143  0.466591  0.317681  0.61229  0.34365  0.38016   \n",
       "1  0.946616  0.836443  0.482425  0.443760  0.71330  0.51890  0.60401   \n",
       "2  0.412789  0.718531  0.212308  0.325779  0.29758  0.34365  0.30529   \n",
       "3  0.354893  0.397069  0.369930  0.342355  0.40028  0.33237  0.31480   \n",
       "4  0.696873  0.302678  0.398862  0.391833  0.23688  0.43731  0.50556   \n",
       "\n",
       "     cont11    cont12    cont13    cont14  \n",
       "0  0.377724  0.369858  0.704052  0.392562  \n",
       "1  0.689039  0.675759  0.453468  0.208045  \n",
       "2  0.245410  0.241676  0.258586  0.297232  \n",
       "3  0.348867  0.341872  0.592264  0.555955  \n",
       "4  0.359572  0.352251  0.301535  0.825823  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 49.6 ms\n"
     ]
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 93.9 ms\n"
     ]
    }
   ],
   "source": [
    "pred = linear_model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 490 ms\n"
     ]
    }
   ],
   "source": [
    "# model.fit(train_x, train['log_loss'])\n",
    "# predicted = model.predict(X)\n",
    "# predicted = np.exp(predicted) \n",
    "submission = pd.DataFrame({\n",
    "       \"id\": test_id,\n",
    "        \"loss\": linear_model.predict(test)\n",
    "    })\n",
    "submission.to_csv('Submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>cat10</th>\n",
       "      <th>cat11</th>\n",
       "      <th>cat12</th>\n",
       "      <th>cat13</th>\n",
       "      <th>cat14</th>\n",
       "      <th>cat15</th>\n",
       "      <th>cat16</th>\n",
       "      <th>cat17</th>\n",
       "      <th>cat18</th>\n",
       "      <th>cat19</th>\n",
       "      <th>cat20</th>\n",
       "      <th>cat21</th>\n",
       "      <th>cat22</th>\n",
       "      <th>cat23</th>\n",
       "      <th>cat24</th>\n",
       "      <th>cat25</th>\n",
       "      <th>cat26</th>\n",
       "      <th>cat27</th>\n",
       "      <th>cat28</th>\n",
       "      <th>cat29</th>\n",
       "      <th>cat30</th>\n",
       "      <th>cat31</th>\n",
       "      <th>cat32</th>\n",
       "      <th>cat33</th>\n",
       "      <th>cat34</th>\n",
       "      <th>cat35</th>\n",
       "      <th>cat36</th>\n",
       "      <th>cat37</th>\n",
       "      <th>cat38</th>\n",
       "      <th>cat39</th>\n",
       "      <th>cat40</th>\n",
       "      <th>cat41</th>\n",
       "      <th>cat42</th>\n",
       "      <th>cat43</th>\n",
       "      <th>cat44</th>\n",
       "      <th>cat45</th>\n",
       "      <th>cat46</th>\n",
       "      <th>cat47</th>\n",
       "      <th>cat48</th>\n",
       "      <th>cat49</th>\n",
       "      <th>cat50</th>\n",
       "      <th>cat51</th>\n",
       "      <th>cat52</th>\n",
       "      <th>cat53</th>\n",
       "      <th>cat54</th>\n",
       "      <th>cat55</th>\n",
       "      <th>cat56</th>\n",
       "      <th>cat57</th>\n",
       "      <th>cat58</th>\n",
       "      <th>cat59</th>\n",
       "      <th>cat60</th>\n",
       "      <th>cat61</th>\n",
       "      <th>cat62</th>\n",
       "      <th>cat63</th>\n",
       "      <th>cat64</th>\n",
       "      <th>cat65</th>\n",
       "      <th>cat66</th>\n",
       "      <th>cat67</th>\n",
       "      <th>cat68</th>\n",
       "      <th>cat69</th>\n",
       "      <th>cat70</th>\n",
       "      <th>cat71</th>\n",
       "      <th>cat72</th>\n",
       "      <th>cat73</th>\n",
       "      <th>cat74</th>\n",
       "      <th>cat75</th>\n",
       "      <th>cat76</th>\n",
       "      <th>cat77</th>\n",
       "      <th>cat78</th>\n",
       "      <th>cat79</th>\n",
       "      <th>cat80</th>\n",
       "      <th>cat81</th>\n",
       "      <th>cat82</th>\n",
       "      <th>cat83</th>\n",
       "      <th>cat84</th>\n",
       "      <th>cat85</th>\n",
       "      <th>cat86</th>\n",
       "      <th>cat87</th>\n",
       "      <th>cat88</th>\n",
       "      <th>cat89</th>\n",
       "      <th>cat90</th>\n",
       "      <th>cat91</th>\n",
       "      <th>cat92</th>\n",
       "      <th>cat93</th>\n",
       "      <th>cat94</th>\n",
       "      <th>cat95</th>\n",
       "      <th>cat96</th>\n",
       "      <th>cat97</th>\n",
       "      <th>cat98</th>\n",
       "      <th>cat99</th>\n",
       "      <th>cat100</th>\n",
       "      <th>cat101</th>\n",
       "      <th>cat102</th>\n",
       "      <th>cat103</th>\n",
       "      <th>cat104</th>\n",
       "      <th>cat105</th>\n",
       "      <th>cat106</th>\n",
       "      <th>cat107</th>\n",
       "      <th>cat108</th>\n",
       "      <th>cat109</th>\n",
       "      <th>cat110</th>\n",
       "      <th>cat111</th>\n",
       "      <th>cat112</th>\n",
       "      <th>cat113</th>\n",
       "      <th>cat114</th>\n",
       "      <th>cat115</th>\n",
       "      <th>cat116</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>269</td>\n",
       "      <td>0.726300</td>\n",
       "      <td>0.245921</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>0.789639</td>\n",
       "      <td>0.310061</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.335060</td>\n",
       "      <td>0.30260</td>\n",
       "      <td>0.67135</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.569745</td>\n",
       "      <td>0.594646</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>0.714843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>85</td>\n",
       "      <td>0.330514</td>\n",
       "      <td>0.737068</td>\n",
       "      <td>0.592681</td>\n",
       "      <td>0.614134</td>\n",
       "      <td>0.885834</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.436585</td>\n",
       "      <td>0.60087</td>\n",
       "      <td>0.35127</td>\n",
       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
       "      <td>0.366307</td>\n",
       "      <td>0.611431</td>\n",
       "      <td>0.304496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>153</td>\n",
       "      <td>0.261841</td>\n",
       "      <td>0.358319</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>0.236924</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.289648</td>\n",
       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>0.321594</td>\n",
       "      <td>0.555782</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.422268</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>0.273204</td>\n",
       "      <td>0.159990</td>\n",
       "      <td>0.527991</td>\n",
       "      <td>0.473202</td>\n",
       "      <td>0.704268</td>\n",
       "      <td>0.178193</td>\n",
       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8  cat9  cat10  cat11  cat12  \\\n",
       "0     0     1     0     1     0     0     0     0     1      0      1      0   \n",
       "1     0     1     0     0     0     0     0     0     1      1      0      0   \n",
       "2     0     1     0     0     1     0     0     0     1      1      1      1   \n",
       "3     1     1     0     1     0     0     0     0     1      0      0      0   \n",
       "4     0     1     0     1     0     0     0     0     1      1      0      1   \n",
       "\n",
       "   cat13  cat14  cat15  cat16  cat17  cat18  cat19  cat20  cat21  cat22  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      1      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat23  cat24  cat25  cat26  cat27  cat28  cat29  cat30  cat31  cat32  \\\n",
       "0      1      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      1      0      0      0      0      0      0      0      0      0   \n",
       "4      1      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat33  cat34  cat35  cat36  cat37  cat38  cat39  cat40  cat41  cat42  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      1      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat43  cat44  cat45  cat46  cat47  cat48  cat49  cat50  cat51  cat52  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat53  cat54  cat55  cat56  cat57  cat58  cat59  cat60  cat61  cat62  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   cat63  cat64  cat65  cat66  cat67  cat68  cat69  cat70  cat71  cat72  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      1   \n",
       "\n",
       "   cat73  cat74  cat75  cat76  cat77  cat78  cat79  cat80  cat81  cat82  \\\n",
       "0      0      0      1      0      3      1      1      3      3      1   \n",
       "1      0      0      0      0      3      1      1      3      3      0   \n",
       "2      0      0      0      0      3      1      1      1      3      1   \n",
       "3      1      0      0      0      3      1      1      3      3      3   \n",
       "4      0      0      0      0      3      1      3      1      3      1   \n",
       "\n",
       "   cat83  cat84  cat85  cat86  cat87  cat88  cat89  cat90  cat91  cat92  \\\n",
       "0      3      2      1      3      1      0      0      0      0      0   \n",
       "1      1      2      1      3      1      0      0      0      0      0   \n",
       "2      3      2      1      1      1      0      0      0      0      0   \n",
       "3      1      2      1      3      1      0      0      0      0      0   \n",
       "4      1      2      1      1      2      0      0      0      1      5   \n",
       "\n",
       "   cat93  cat94  cat95  cat96  cat97  cat98  cat99  cat100  cat101  cat102  \\\n",
       "0      3      1      2      4      0      2     15       1       6       0   \n",
       "1      3      3      2      4      4      3     15      11       5       0   \n",
       "2      3      3      2      4      4      0      1      11      14       0   \n",
       "3      3      3      2      4      4      3     15       8       3       0   \n",
       "4      3      1      3      4      4      0     12       5       9       0   \n",
       "\n",
       "   cat103  cat104  cat105  cat106  cat107  cat108  cat109  cat110  cat111  \\\n",
       "0       0       8       4       6       9       6      45      28       2   \n",
       "1       0       4       4       8      10      10      33      65       0   \n",
       "2       1       4       5       7       5       0       2      85       0   \n",
       "3       0       4       4       8      10      10      33      67       2   \n",
       "4       0       3       4      10       6       1      66      50       2   \n",
       "\n",
       "   cat112  cat113  cat114  cat115  cat116     cont1     cont2     cont3  \\\n",
       "0      19      55       0      14     269  0.726300  0.245921  0.187583   \n",
       "1      22      38       0      14      85  0.330514  0.737068  0.592681   \n",
       "2      28       5       0       8     153  0.261841  0.358319  0.484196   \n",
       "3      39       4       0      14      79  0.321594  0.555782  0.527991   \n",
       "4      50      38       0      10      55  0.273204  0.159990  0.527991   \n",
       "\n",
       "      cont4     cont5     cont6     cont7    cont8    cont9   cont10  \\\n",
       "0  0.789639  0.310061  0.718367  0.335060  0.30260  0.67135  0.83510   \n",
       "1  0.614134  0.885834  0.438917  0.436585  0.60087  0.35127  0.43919   \n",
       "2  0.236924  0.397069  0.289648  0.315545  0.27320  0.26076  0.32446   \n",
       "3  0.373816  0.422268  0.440945  0.391128  0.31796  0.32128  0.44467   \n",
       "4  0.473202  0.704268  0.178193  0.247408  0.24564  0.22089  0.21230   \n",
       "\n",
       "     cont11    cont12    cont13    cont14  \n",
       "0  0.569745  0.594646  0.822493  0.714843  \n",
       "1  0.338312  0.366307  0.611431  0.304496  \n",
       "2  0.381398  0.373424  0.195709  0.774425  \n",
       "3  0.327915  0.321570  0.605077  0.602642  \n",
       "4  0.204687  0.202213  0.246011  0.432606  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 94.4 ms\n"
     ]
    }
   ],
   "source": [
    "X = train[train.columns.delete(-1)]\n",
    "X = X[X.columns.delete(-1)]\n",
    "y = train['loss']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model complete\n",
      "[1682.66814319 1356.47267799 2980.6050236  ...  993.89405177 1532.73550776\n",
      " 2291.45267582]\n",
      "RMSE:2193.6829\n",
      "Training data mean absolute error:  1278.0637246042866\n",
      "time: 28.5 s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "X = train[train.columns.delete(-1)]\n",
    "X = X[X.columns.delete(-1)]\n",
    "y = train['loss']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X , y, test_size=0.3, random_state=42)\n",
    "model_SVR = LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive',\n",
    "fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0,\n",
    "random_state=None, max_iter=1000)\n",
    "model_SVR.fit(X_train, y_train)\n",
    "predicted = model_SVR.predict(X_test)\n",
    "print(\"Model complete\")\n",
    "print(predicted)\n",
    "mae = mean_absolute_error(predicted, y_test)\n",
    "print(\"RMSE:{:.4f}\".format(np.sqrt(mean_squared_error(predicted,y_test))))\n",
    "print(\"Training data mean absolute error: \",mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 43 s\n"
     ]
    }
   ],
   "source": [
    "model_SVR.fit(X,y)\n",
    "submission = pd.DataFrame({\n",
    "       \"id\": test_id,\n",
    "        \"loss\": model_SVR.predict(test)\n",
    "    })\n",
    "submission.to_csv('Submission2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_absolute_error as mae, make_scorer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy.sparse import csr_matrix, hstack\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../data_files/4.Allstate_data/train.csv')\n",
    "test = pd.read_csv('../data_files/4.Allstate_data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical:116 features\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计离散型变量的数目\n",
    "cat_features = list(train.select_dtypes(include=['object']).columns)\n",
    "print('Categorical:{} features'.format(len(cat_features)))\n",
    "train['log_loss'] = np.log(train['loss'])\n",
    "features = [x for x in train.columns if x not in ['id','loss','log_loss']]\n",
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.shape\n",
    "X = train[features]\n",
    "y = train['loss']\n",
    "for c in range(len(cat_features)):\n",
    "    X[cat_features[c]] = X[cat_features[c]].astype('category').cat.codes\n",
    "X_train, X_test, y_train, y_test = train_test_split(X , y, test_size=0.3, random_state=42)\n",
    "test_id = test['id']\n",
    "test.drop('id',axis=1,inplace=True)\n",
    "for c in range(len(cat_features)):\n",
    "    test[cat_features[c]] = test[cat_features[c]].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def xg_eval_mae(yhat,dtrain):\n",
    "#     y = dtrain.get_label()\n",
    "#     return 'mae',mae(np.exp(y),np.exp(yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dtrain = xgb.DMatrix(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgb_params = {\n",
    "#     'seed':0,\n",
    "#     'eta':0.1,\n",
    "#     'colsample_bytree':0.5,\n",
    "#     'silent':1,\n",
    "#     'subsample':0.5,\n",
    "#     'objective':'reg:linear',\n",
    "#     'max_depth':5,\n",
    "#     'min_child_weight':3\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bst_cv1 = xgb.cv(xgb_params,dtrain,num_boost_round=50,nfold=3,seed=0,feval=xg_eval_mae,maximize=False,early_stopping_rounds=10)\n",
    "# print('CV score',bst_cv1.iloc[-1,:]['test-mae-mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure()\n",
    "# bst_cv1[['train-mae-mean','test-mae-mean']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bst_cv2 = xgb.cv(xgb_params,dtrain,num_boost_round=100,nfold=3,seed=0,feval=xg_eval_mae,maximize=False,early_stopping_rounds=10)\n",
    "# print('CV score',bst_cv2.iloc[-1,:]['test-mae-mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "# fig.set_size_inches(16,4)\n",
    "\n",
    "# ax1.set_title('100 rounds of training')\n",
    "# ax1.set_xlabel('Rounds')\n",
    "# ax1.set_ylabel('Loss')\n",
    "# ax1.grid(True)\n",
    "# ax1.plot(bst_cv2[['train-mae-mean', 'test-mae-mean']])\n",
    "# ax1.legend(['Training Loss', 'Test Loss'])\n",
    "\n",
    "# ax2.set_title('60 last rounds of training')\n",
    "# ax2.set_xlabel('Rounds')\n",
    "# ax2.set_ylabel('Loss')\n",
    "# ax2.grid(True)\n",
    "# ax2.plot(bst_cv2.iloc[40:][['train-mae-mean', 'test-mae-mean']])\n",
    "# ax2.legend(['Training Loss', 'Test Loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = xgb.XGBRegressor(n_estimators=1000,learning_rate=0.01)\n",
    "reg.fit(X_train,y_train)\n",
    "reg_predict = reg.predict(X_test)\n",
    "print('mse = {}'.format(mae(reg_predict,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg.fit(X,y)\n",
    "submission = pd.DataFrame({\n",
    "       \"id\": test_id,\n",
    "        \"loss\": np.exp(reg.predict(test))\n",
    "    })\n",
    "submission.to_csv('Submission3_xgboost.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lr_predict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-17b099a7e285>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mreg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mreg_predict2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmae\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mse = {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmae\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RMSE:{:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg_predict2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'lr_predict' is not defined"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2h 45min 35s\n"
     ]
    }
   ],
   "source": [
    "reg2 = xgb.XGBRegressor(reg_alpha=0.2,  n_estimators=10000, learning_rate=0.05)\n",
    "reg2 = reg2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_predict2 = reg2.predict(X_test)\n",
    "mae = mae(reg_predict2, y_test)\n",
    "print('mse = {}'.format(mae))\n",
    "print(\"RMSE:{:.4f}\".format(np.sqrt(mean_squared_error(reg_predict2, y_test))))\n",
    "print(\"Training data mean absolute error: \",mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "       \"id\": test_id,\n",
    "        \"loss\": reg2.predict(test)\n",
    "    })\n",
    "submission.to_csv('Submission4_xgboost.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor as gBR\n",
    "reg3 = gBR()\n",
    "reg3 = reg3.fit(X_train, y_train)\n",
    "predict = reg.predict(y_test)\n",
    "mae = mae(predict, y_test)\n",
    "print('mse = {}'.format(mae))\n",
    "print(\"RMSE:{:.4f}\".format(np.sqrt(mean_squared_error(predict, y_test))))\n",
    "print(\"Training data mean absolute error: \",mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error as mae,mean_squared_error as mse\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "path = u'../data_files/4.Allstate_data/'\n",
    "train = pd.read_csv(path+'train.csv')\n",
    "test = pd.read_csv(path+'test.csv')\n",
    "\n",
    "train['log_loss'] = np.log(train['loss'])\n",
    "cat_features = list(train.select_dtypes(include=['object']).columns)\n",
    "print('Categorical:{} features'.format(len(cat_features)))\n",
    "train['log_loss'] = np.log(train['loss'])\n",
    "features = [x for x in train.columns if x not in ['id','loss','log_loss']]\n",
    "len(features)\n",
    "\n",
    "X = train[features]\n",
    "y = train['log_loss']\n",
    "for c in range(len(cat_features)):\n",
    "    X[cat_features[c]] = X[cat_features[c]].astype('category').cat.codes\n",
    "\n",
    "test_id = test['id']\n",
    "test.drop('id',axis=1,inplace=True)\n",
    "for c in range(len(cat_features)):\n",
    "    test[cat_features[c]] = test[cat_features[c]].astype('category').cat.codes\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=0)\n",
    "\n",
    "n_estimators= 1000\n",
    "reg = xgboost.XGBRegressor(n_estimators=n_estimators,learning_rate=0.05)\n",
    "reg.fit(X_train,y_train)\n",
    "print('model Complete!')\n",
    "pred = np.exp(reg.predict(X_test))\n",
    "print('mae = {}'.format(mae(pred,np.exp(y_test))))\n",
    "print('rmse = {}'.format(np.sqrt(mse(pred,np.exp(y_test)))))\n",
    "\n",
    "reg_full = xgboost.XGBRegressor(n_estimators=n_estimators,learning_rate=0.05)\n",
    "reg_full.fit(X,y)\n",
    "result = pd.DataFrame({\n",
    "    'id':test_id,\n",
    "    'loss':np.exp(reg_full.predict(test))\n",
    "})\n",
    "\n",
    "result.to_csv('result.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error as mae,mean_squared_error as mse\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "path = u'D:/学习/数据科学导论/数据科学导论大作业/data files/allstate-claims-severity/'\n",
    "train = pd.read_csv(path+'train.csv')\n",
    "test = pd.read_csv(path+'test.csv')\n",
    "\n",
    "train['log_loss'] = np.log(train['loss'])\n",
    "cat_features = list(train.select_dtypes(include=['object']).columns)\n",
    "print('Categorical:{} features'.format(len(cat_features)))\n",
    "train['log_loss'] = np.log(train['loss'])\n",
    "features = [x for x in train.columns if x not in ['id','loss','log_loss']]\n",
    "len(features)\n",
    "\n",
    "X = train[features]\n",
    "y = train['log_loss']\n",
    "for c in range(len(cat_features)):\n",
    "    X[cat_features[c]] = X[cat_features[c]].astype('category').cat.codes\n",
    "\n",
    "test_id = test['id']\n",
    "test.drop('id',axis=1,inplace=True)\n",
    "for c in range(len(cat_features)):\n",
    "    test[cat_features[c]] = test[cat_features[c]].astype('category').cat.codes\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=0)\n",
    "\n",
    "# n_estimators= 700\n",
    "# reg = xgboost.XGBRegressor(n_estimators=n_estimators,learning_rate=0.05)\n",
    "# reg.fit(X_train,y_train)\n",
    "# print('model Complete!')\n",
    "# pred = np.exp(reg.predict(X_test))\n",
    "# print('mae = {}'.format(mae(pred,np.exp(y_test))))\n",
    "# print('rmse = {}'.format(np.sqrt(mse(pred,np.exp(y_test)))))\n",
    "#\n",
    "# reg_full = xgboost.XGBRegressor(n_estimators=n_estimators,learning_rate=0.05)\n",
    "# reg_full.fit(X,y)\n",
    "# n_estimators= 700\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor as gBR\n",
    "n_estimators= 100\n",
    "reg2 = gBR(n_estimators=n_estimators,learning_rate=0.05)\n",
    "reg2 = reg2.fit(X_train, y_train)\n",
    "\n",
    "print('model Complete!')\n",
    "pred = np.exp(reg2.predict(X_test))\n",
    "print('mae = {}'.format(mae(pred,np.exp(y_test))))\n",
    "print('rmse = {}'.format(np.sqrt(mse(pred,np.exp(y_test)))))\n",
    "\n",
    "reg2_full = gBR(n_estimators=n_estimators,learning_rate=0.05)\n",
    "reg2_full.fit(X,y)\n",
    "result = pd.DataFrame({\n",
    "    'id':test_id,\n",
    "    'loss':np.exp(reg2_full.predict(test))\n",
    "})\n",
    "\n",
    "result.to_csv('result.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xgboost\n",
    "# from xgboost import XGBClassifier \n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# seed = 7\n",
    "# test_size = 0.33\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed)\n",
    "# model = XGBClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "# # make predictions for test data\n",
    "# y_pred = model.predict(X_test)\n",
    "# predictions = [round(value) for value in y_pred]\n",
    "# # evaluate predictions\n",
    "# accuracy = accuracy_score(y_test, predictions)\n",
    "# print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission = pd.DataFrame({\n",
    "#        \"id\": test_id,\n",
    "#         \"loss\": model.predict(test)\n",
    "#     })\n",
    "# submission.to_csv('Submission3_xgboost.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg = xgboost.XGBRegressor(reg_alpha=0.2,  n_estimators=10000, learning_rate=0.05)\n",
    "# reg = reg.fit(X_train, y_train)\n",
    "# lr_predict = reg.predict(X_test)\n",
    "# mae = mean_absolute_error(lr_predict, y_test)\n",
    "# print(\"RMSE:{:.4f}\".format(np.sqrt(mean_squared_error(lr_predict, y_test))))\n",
    "# print(\"Training data mean absolute error: \",mae)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
